Explicitly Color-Inspired Neural Style Transfer Using

Arbitrary style transfer aims to perceptually reflect the style of a reference image in artistic creations with visual aesthetics.Traditional style transfer models,particularly those using adaptive instance normalization(AdaIN)layer,rely on global statistics,which often fail to capture the spatially local color distribution,leading to outputs that lack variation despite geometric transformations.To address this,we introduce Patchified AdaIN,a color-inspired style transfer method that applies AdaIN to localized patches,utilizing local statistics to capture the spatial color distribution of the reference image.This approach enables enhanced color awareness in style transfer,adapting dynamically to geometric transformations by leveraging local image statistics.Since Patchified AdaIN builds on AdaIN,it integrates seamlessly into existing frameworks without the need for additional training,allowing users to control the output quality through adjustable blending parameters.Our comprehensive experiments demonstrate that Patchified AdaIN can reflect geometric transformations(e.g.,translation,rotation,flipping)of images for style transfer,thereby achieving superior results compared to state-of-the-art methods.Additional experiments show the compatibility of Patchified AdaIN for integration into existing networks to enable spatial color-aware arbitrary style transfer by replacing the conventional AdaIN layer with the Patchified AdaIN layer.

Single-image night haze removal based on color channel transfer and estimation of spatial variation in atmospheric light

The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.

The Influence of Energy Transfer on the Color Temperature Change in Color-Tunable Organic Light Emitting Diodes with Interface Exciplex

The color-tunable white organic light-emitting diode (CT-WOLED) with wide correlation color temperature (CCT) has many advantages in optimizing the artificial light source to adapt to the human physiological cycle. The research on the change trend of CCT and the law of extending the change range of CCT will help to further improve the performance of this kind of device. The present work fabricated a series of CT-WOLED devices with a simple structure, which are all composed of two ultra-thin phosphor layers (PO-01 and Flrpic) and a spacer interlayer. The yellow interface exciplex (TCTA/PO-T2T) formed between the spacer layer (PO-T2T) and transmission material (TCTA) in EML will decrease the CCT value at low voltage. The relationship between the energy transfer in EML and CCT change trend is investigated by adjusting the interface exciplexes and the thickness of the interlayer or the phosphor layer in devices A, B and C, respectively. The results demonstrate that a simple OLED device with an interlayer inserted between two ultra-thin phosphor layers can achieve a wider CCT span from 3359 K to 6451 K at voltage increases from 2.75 V to 8.25 V. .

Non-volatile dynamically switchable color display via chalcogenide stepwise cavity resonators

High-resolution multi-color printing relies upon pixelated optical nanostructures,which is crucial to promote color display by producing nonbleaching colors,yet requires simplicity in fabrication and dynamic switching.Antimony trisulfide(Sb_(2)S_(3))is a newly rising chalcogenide material that possesses prompt and significant transition of its optical characteristics in the visible region between amorphous and crystalline phases,which holds the key to color-varying devices.Herein,we proposed a dynamically switchable color printing method using Sb_(2)S_(3)-based stepwise pixelated Fabry-Pérot(FP)cavities with various cavity lengths.The device was fabricated by employing a direct laser patterning that is a less timeconsuming,more approachable,and low-cost technique.As switching the state of Sb_(2)S_(3) between amorphous and crystalline,the multi-color of stepwise pixelated FP cavities can be actively changed.The color variation is due to the profound change in the refractive index of Sb_(2)S_(3) over the visible spectrum during its phase transition.Moreover,we directly fabricated sub-50 nm nano-grating on ultrathin Sb_(2)S_(3) laminate via microsphere 800-nm femtosecond laser irradiation in far field.The minimum feature size can be further decreased down to~45 nm(λ/17)by varying the thickness of Sb_(2)S_(3) film.Ultrafast switchable Sb_(2)S_(3) photonic devices can take one step toward the next generation of inkless erasable papers or displays and enable information encryption,camouflaging surfaces,anticounterfeiting,etc.Importantly,our work explores the prospects of rapid and rewritable fabrication of periodic structures with nano-scale resolution and can serve as a guideline for further development of chalcogenide-based photonics components.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Coupling of reduced inorganic fertilizer with plant-based organic fertilizer as a promising fertilizer management strategy for colored rice in tropical regions

Colored rice is a type of high-quality,high-added-value rice that has attracted increasing attention in recent years.The use of large amounts of inorganic nitrogen fertilizer in rice fields results in low fertilizer use efficiency and high environmental pollution.Organic fertilizer is a promising way to improve soil quality and sustain high yields.However,most studies focus on the effect of animal-based organic fertilizers.The effects of different ratios of plantbased organic fertilizer and inorganic fertilizer on the grain yield and quality of colored rice have rarely been reported.Therefore,a two-year field experiment was conducted in 2020 and 2021 to study the effects of replacing inorganic N fertilizers with plant-based organic fertilizers on the yield,nitrogen use efficiency(NUE),and anthocyanin content of two colored rice varieties in a tropical region in China.The experimental treatments included no nitrogen fertilization(T1),100% inorganic nitrogen fertilizer(T2),30%inorganic nitrogen fertilizer substitution with plant-based organic fertilizer(T3),60%inorganic nitrogen fertilizer substitution with plant-based organic fertilizer(T4),and 100% plantbased organic fertilizer(T5).The total nitrogen provided to all the treatments except T1 was the same at 120 kg ha-1.Our results showed that the T3 treatment enhanced the grain yield and anthocyanin content of colored rice by increasing nitrogen use efficiency compared with T2.On average,grain yields were increased by 9 and 8%,while the anthocyanin content increased by 16 and 10% in the two colored rice varieties under T3 across the two years,respectively,as compared with T2.Further study of the residual effect of partial substitution of inorganic fertilizers showed that the substitution of inorganic fertilizer with plant-based organic fertilizer improved the soil physiochemical properties,and thus increased the rice grain yield,in the subsequent seasons.The highest grain yield of the subsequent rice crop was observed under the T5 treatment.Our results suggested that the application of plantbased organic fertilizers can sustain the production of colored rice with high anthocyanin content in tropical regions,which is beneficial in reconciling the relationship between rice production and environmental protection.

Causal Analysis Between Rice Growth and Cadmium Accumulation and Transfer under Arbuscular Mycorrhizal Inoculation

Cadmium(Cd)contamination in rice has been a serious threat to human health.To investigate the effects of arbuscular mycorrhizal fungi(AMF)on the Cd translocation in rice,a controlled pot experiment was conducted.The results indicated that AMF significantly increased rice biomass,with an increase of up to 40.0%,particularly in root biomass by up to 68.4%.Notably,the number of prominent rice individuals also increased,and their plasticity was enhanced following AMF inoculation.AMF led to an increase in the net photosynthetic rate and antioxidant enzyme activity of rice.In the AMF treatment group,the Cd concentration in the rice roots was significantly higher(19.1%‒68.0%)compared with that in the control group.Conversely,the Cd concentration in the rice seeds was lower in the AMF treatment group,indicating that AMF facilitated the sequestration of Cd in rice roots and reduced Cd accumulation in the seeds.Path coefficients varied across different treatments,suggesting that AMF inoculation reduced the direct impact of soil Cd concentration on the total Cd accumulation in seeds.The translocation of Cd was consistently associated with simultaneous growth dilution and compensatory accumulation as a result of mycorrhizal effects.Our study quantitatively analyzed this process through path analysis and clarified the causal relationship between rice growth and Cd transfer under the influence of AMF.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Generation of mitochondrial replacement monkeys by female pronucleus transfer

Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

Coherent optical frequency transfer via 972-km fiber link

We demonstrate coherent optical frequency dissemination over a distance of 972 km by cascading two spans where the phase noise is passively compensated for.Instead of employing a phase discriminator and a phase locking loop in the conventional active phase control scheme,the passive phase noise cancellation is realized by feeding double-trip beat-note frequency to the driver of the acoustic optical modulator at the local site.This passive scheme exhibits fine robustness and reliability,making it suitable for long-distance and noisy fiber links.An optical regeneration station is used in the link for signal amplification and cascaded transmission.The phase noise cancellation and transfer instability of the 972-km link is investigated,and transfer instability of 1.1×10^(-19)at 10^(4)s is achieved.This work provides a promising method for realizing optical frequency distribution over thousands of kilometers by using fiber links.

The protective effect of cyclodextrin on the color quality and stability of Cabernet Sauvignon red wine

The impact of cyclodextrins(CDs)on wine quality and stability remains largely unknown.This study systematically assessed the protective effect of the post-fermentation addition of CDs on color stability of red wine from the viewpoints of color characteristics,copigmentation and phenolic profiles.The grey relational analysis(GRA)and principal component analysis(PCA)methods were employed to dissect the key effective determinants related to color quality.The addition of CDs induced a significant hyperchromic effect of 8.19-25.40%,a significant bathochromic effect and an enhancement of the color intensity.Furthermore,the evolution of anthocyanin forms and the content of monomeric anthocyanins revealed that β-CD is a superior favorable cofactor during wine aging,but for long-term aging,2-HP-β-CD and 2-HP-γ-CD are more beneficial in promoting the formation of polymerized anthocyanins and color stability.This work provides an important reference for the use of CDs to enhance the color quality and stability of red wines.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

The regulation of ferrocene-based catalysts on heat transfer in highpressure combustion of ammonium perchlorate/hydroxyl-terminated polybutadiene/aluminum composite propellants

The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based death.Conventionally,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical images.This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)algorithm.The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer.To accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image quality.In addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed images.Moreover,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal cancer.The utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

Synthesis and Coloring Mechanism of Metallic Glaze in R_(2)O-RO-Al_(2)O_(3)-SiO_(2)-P_(2)O_(5)System

A copper-red and silver-white metallic glaze of R_(2)O-RO-Al_(2)O_(3)-SiO_(2)-P_(2)O_(5)system was synthesized by adjusting the firing temperature and glaze components.The coloration mechanism of the metallic glaze was revealed via investigation of the microstructure of the glaze.Our research reveals that the metallic glaze with different colors is mainly due to the amount of Fe_(2)O_(3).The metallic glaze shows a silver-white luster due to a structural color ofα-Fe_(2)O_(3)crystals with a good orientation when the sample contains 0.0939 mol of Fe_(2)O_(3),maintaining temperatures at 1150℃for 0.5 h.The metallic glaze is copper-red which is dominated by the coupling of chemical and structural color ofα-Fe_(2)O_(3)crystals when the sample contains 0.0783 mol of Fe_(2)O_(3).After testing the amount of SiO_(2),we find that 4.0499 mol is the optimal amount to form the ceramic network,and 0.27 mol AlPO_(4)is the best amount to promote phase separation.

Physiological and psychological responses to tended plant communities with varying color characteristics

Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,with combinations of yellow,green,and red foliage,as experimental stimuli.A total of 27 images were used as visual stimuli.We used electroencephalography to measureαwave activity(8-13 Hz)in 40 subjects while they viewed visual stimuli.These data were combined with subjective questionnaire data to analyze the relaxing effect of images of tended plant communities with different color types and proportions on people.The results revealed that,although there were slight differences between the electroencephalography and psychological findings,women were significantly more relaxed than men after viewing the images.Physiological and psychological responses varied with the types and proportions of colors in the tended plant communities:those of foliage with combinations of two or three colors induced stronger responses than images with a single color.Specifically,(1)for one-color plant communities,green or yellow plant communities induced a stronger relaxation effect than red plant communities;(2)for two-color plant communities,the optimal color proportion was 55%+45%,and the green+yellow and green+red color combinations induced a stronger relaxation effect;(3)for three-color plant communities,the relaxation effect was strongest when the color proportion was 55%green+25%yellow+20%red.These data would provide a plant color matching in future plant landscape design,which may be helpful for creating healthy and relaxing environments.

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.