Design of Content-Based Retrieval System in Remote Sensing Image Database

To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image application firstly, and then the algorithm adopted for feature extraction and multidimensional indexing, and relevance feedback by this model are analyzed in detail. Finally, the contents intending to be researched about this model are proposed.

Content-based retrieval based on binary vectors for 2-D medical images

In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...

Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.

A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Auto-expanded multi query examples technology in content-based image retrieval

In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

Ontology Framework for Content-Based 3D Model Retrieval

The goal of the research on ontology framework for content-based 3D model retrieval is to develop a rich set of 3D model semantic representation so that both humans and machines can generate and understand model descriptions and processing for fast efficient retrieval from model collections. The purpose of ontology development for content-based 3D model retrieval is intended to describe model information regardless of storage, feature extraction and creation. The ontology includes the information on media features, low level visual descriptors, non media features of 3D model and their relationships. It is implemented in protege 3.1.

Comparison between ozonesonde measurements and satellite retrievals over Beijing,China

从2013年开始,作者团队使用自主研发电化学原理臭氧探空仪在华北平原北京地区进行每周一次观测.本研究首次使用2013-2019年期间北京地区臭氧探空数据评估Aqua卫星搭载大气红外探测仪(AIRS)和Aura卫星搭载微波临边探测器(MLS)反演垂直臭氧廓线,并对比臭氧探空,AIRS和Aura卫星搭载臭氧监测仪(OMI)臭氧柱总量结果.尽管臭氧探空与卫星反演垂直臭氧廓线在局部高度处差异较大,但整体来说两者较为接近(相对偏差大多<10%).臭氧探空,AIRS和OMI三种仪器测量臭氧柱总量的年变化特征较为一致,其年均臭氧柱总量分别为351.8±18.4 DU,348.8±19.5 DU和336.9±14.2 DU.后续对国内多站点观测数据分析将有助于进一步理解臭氧探空与卫星反演臭氧资料在不同区域的一致性.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

On the Nature of Online Retrieval of Electronic Data

With the development of information technology,the online retrieval of remote electronic data has become an important method for investigative agencies to collect evidence.In the current normative documents,the online retrieval of electronic data is positioned as a new type of arbitrary investigative measure.However,study of its actual operation has found that the online retrieval of electronic data does not fully comply with the characteristics of arbitrary investigative measures.The root cause is its inaccurately defined nature due to analogy errors,an emphasis on the authenticity of electronic data at the cost of rights protection,insufficient effectiveness of normative documents to break through the boundaries of law,and superficial inconsistency found in the mechanical comparison with the nature of existing investigative measures causes.The nature of electronic data retrieved online should be defined according to different circumstances.The retrieval of electronic data disclosed on the Internet is an arbitrary investigative measure,and following procedural specifications should be sufficient.When investigators conceal their true identities and enter the cyberspace of the suspected crime through a registered account to extract dynamic electronic data for criminal activities,it is essentially a covert investigation in cyberspace,and they should follow the normative requirements for covert investigations.The retrieval of dynamic electronic data from private spaces is a technical investigative measure and should be implemented in accordance with the technical investigative procedures.Retrieval of remote“non-public electronic data involving privacy”is a mandatory investigative measure,and is essentially a search in the virtual space.Therefore,procedural specifications should be set in accordance with the standards of searching.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively.And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way.Image feature matching mainly depends on the similarity between two image feature vectors.In this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method.Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image.And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image.Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image.Extensive experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data set.The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

Content-based Image Retrieval Using Color Histogram

This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hue and saturation to extract and represent color information of an image is presented. We also improve the Euclidean-distance algorithm by adding Center of Color to it. The experiment shows modifications made to Euclidean-distance signif-icantly elevate the quality and efficiency of retrieval.

A Content-Based Parallel Image Retrieval System on Cluster Architectures

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.

New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time.