EFFECTIVE FEATURE ANALYSIS FOR COLOR IMAGE SEGMENTATION

An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.

Color Image Segmentation Based on HSI Model

he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is reported. It is in contrast to the conventional approaches by using the three components of HSI color model in succession. This strategy makes the segmentation procedure much fast and effective. Experimental results with typical “headandshoulders” real images taken from videophone sequences show that the new appproach can fulfill the application requirements.

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm

In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.

Tongue image segmentation and tongue color classification based on deep learning

Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.

Color Cell Image Segmentation Based on Chan-Vese Model for Vector-Valued Images

In this paper, we propose a color cell image segmentation method based on the modified Chan-Vese model for vectorvalued images. In this method, both the cell nuclei and cytoplasm can be served simultaneously from the color cervical cell image. Color image could be regarded as vector-valued images because there are three channels, red, green and blue in color image. In the proposed color cell image segmentation method, to segment the cell nuclei and cytoplasm precisely in color cell image, we should use the coarse-fine segmentation which combined the auto dual-threshold method to separate the single cell connection region from the original image, and the modified C-V model for vectorvalued images which use two independent level set functions to separate the cell nuclei and cytoplasm from the cell body. From the result we can see that by using the proposed method we can get the nuclei and cytoplasm region more accurately than traditional model.

Adaptive Segmentation for Unconstrained Iris Recognition

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

Hierarchical Image Segmentation Using a Combined Geometrical and Feature Based Approach

This paper presents a fully automatic segmentation algorithm based on geometrical and local attributes of color images. This method incorporates a hierarchical assessment scheme into any general segmentation algorithm for which the segmentation sensitivity can be changed through parameters. The parameters are varied to create different segmentation levels in the hierarchy. The algorithm examines the consistency of segments based on local features and their relationships with each other, and selects segments at different levels to generate a final segmentation. This adaptive parameter variation scheme provides an automatic way to set segmentation sensitivity parameters locally according to each region's characteristics instead of the entire image. The algorithm does not require any training dataset. The geometrical attributes can be defined by a shape prior for specific applications, i.e. targeting objects of interest, or by one or more general constraint(s) such as boundaries between regions for non-specific applications. Using mean shift as the general segmentation algorithm, we show that our hierarchical approach generates segments that satisfy geometrical properties while conforming with local properties. In the case of using a shape prior, the algorithm can cope with partial occlusions. Evaluation is carried out on the Berkeley Segmentation Dataset and Benchmark (BSDS300) (general natural images) and on geo-spatial images (with specific shapes of interest). The F-measure for our proposed algorithm, i.e. the harmonic mean between precision and recall rates, is 64.2% on BSDS300, outperforming the same segmentation algorithm in its standard non-hierarchical variant.

A Bintree Energy Approach for Colour Image Segmentation Using Adaptive Channel Selection

A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the 'best' channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.

Microscopic Halftone Image Segmentation

Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f 1f 2f 3 color space is superior to the rest.

RESEARCH ON SEGMENTATION OF WEED IMAGES BASED ON COMPUTER VISION

In this letter, a segment algorithm based on color feature of images is proposed. The al- gorithm separates the weed area from soil background according to the color eigenvalue, which is obtained by analyzing the color difference between the weeds and background in three color spaces RGB, rgb and HSI. The results of the experiment show that it can get notable effect in segmentation according to the color feature, and the possibility of successful segmentation is 87%-93%. This method can also be widely used in other fields which are complicated in the background of the image and facilely influenced in illumination, such as weed identification, tree species discrimination, fruit picking and so on.

A Semi-Vectorial Morphological Segmentation Multi-Component Images of Coumarins on Thin Layer Combined with Laser for Better Separation

In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values atR = 91.20 in red, atB = 213.21 in blue showed the presence of samidin in the extract. The color entropy values HG = 5.25 in green and HB = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.

Color-texture segmentation using JSEG based on Gaussian mixture modeling

An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

A Study on the Influence of Luminance L* in the L*a*b* Color Space during Color Segmentation

In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.

Text Extraction in Complex Color Document Images for Enhanced Readability

Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture;and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.

Color and Texture Segmentation Using an Unified MRF Model

The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I1, I2, I3) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).