Content-based Image Retrieval Algorithms Research Articles

Content-based Image Retrieval using Image Partitioning with Color Histogram and Wavelet-based Color Histogram of the Image

paper presents two content-based image retrieval algorithms that are based on image partitioning. The retrieval in the first algorithm is based only on the image color feature represented by the color histogram, while the retrieval in the second one is based on the image color and texture features represented by the color histogram and Haar wavelet transform, respectively. In these algorithms, each image in the database and the query image are divided into 4-equal sized blocks. Color and texture features are extracted for each block. Distances between the blocks of the query image and the blocks of a database image are calculated, then, the similarity between the query image and the database image is calculated by finding the minimum cost matching based on most similar highest priority (MSHP) principle. A CBIR system that implements the proposed algorithms has been developed. To evaluate the effectiveness of the proposed algorithms, experiments have been carried out using different color quantization schemes for three different color spaces (HSV, YIQ and YCbCr) with two similarity measures, namely the Histogram Euclidean Distance and Histogram Intersection Distance. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments.

A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval.

Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research. However, clinical adoption of CBIR is partially hindered by the difference between the computed image similarity and the user's search intent, the semantic gap, with the end result that relevant images with outlier features may not be retrieved. Furthermore, most CBIR algorithms do not provide intuitive explanations as to why the retrieved images were considered similar to the query (e.g., which subset of features were similar), hence, it is difficult for users to verify if relevant images, with a small subset of outlier features, were missed. Users, therefore, resort to examining irrelevant images and there are limited opportunities to discover these "missed" images. In this paper, we propose a new approach to medical CBIR by enabling a guided visual exploration of the search space through a tool, called visual analytics for medical image retrieval (VAMIR). The visual analytics approach facilitates interactive exploration of the entire dataset using the query image as a point-of-reference. We conducted a user study and several case studies to demonstrate the capabilities of VAMIR in the retrieval of computed tomography images and multimodality positron emission tomography and computed tomography images.

Content-based histopathology image retrieval using CometCloud.

BackgroundThe development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance.ResultsThe CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers.ConclusionsIn this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Novel Approach to Content Based Image Retrieval Using Evolutionary Computing

Content Based Image Retrieval (CBIR) is an active research area in multimedia domain in this era of information technology. One of the challenges of CBIR is to bridge the gap between low level features and high level semantic. In this study we investigate the Particle Swarm Optimization (PSO), a stochastic algorithm and Genetic Algorithm (GA) for CBIR to overcome this drawback. We proposed a new CBIR system based on the PSO and GA coupled with Support Vector Machine (SVM). GA and PSO both are evolutionary algorithms and in this study are used to increase the number of relevant images. SVM is used to perform final classification. To check the performance of the proposed technique, rich experiments are performed using coral dataset. The proposed technique achieves higher accuracy compared to the previously introduced techniques (FEI, FIRM, simplicity, simple HIST and WH).

A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

spaces, color histograms, histogram distance measurements, size and quantization play an important role in retrieving images based on similarities. This paper presents a study of the effect of color quantization schemes for different color spaces (HSV, YIQ and YCbCr) on the performance of content-based image retrieval (CBIR), using different histogram distance measurements (Histogram Euclidean Distance and Histogram Intersection Distance). For the purpose of this study, a CBIR system that implements two content-based image retrieval algorithms has been developed. The first algorithm is based only on the color feature, while the second one is based on combination of the color and texture features. The color histogram is used for image color feature extraction and Haar wavelet transform is used for image texture feature extraction. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments of this study. The experimental results show which histogram distance measurement is best, which color space gives better retrieval precision, and the best quantization schemes for the considered color spaces, when using only the color feature, and when using a combination of the color and texture features.

Content-based image retrieval using high-dimensional information geometry

In this paper, a new content-based image retrieval approach is proposed based on high-dimensional information theory. The proposed approach overcomes the disadvantages of the current content-based image retrieval algorithms that suffer from the semantic gap. First, we present a new multidimensional information space’s vector angle cosine algorithm of high-dimensional geometry, then, we provide a detailed description of our images retrieval method including proposal of an overlapping image block method and definition of a similarity degree between images on the non-dimensional information subspaces. Finally, experimental results show the higher retrieval efficiency of the proposed algorithm.

Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise.

An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification

In content-based image retrieval (CBIR), the gap between low-level visual features and high-level semantic meanings usually leads to poor performance, and relevance feedback (RF) is an effective method to bridge this gap and to scale up the performance in CBIR systems. In recent years, the support vector machine (SVM) based relevance feedbacks have been popular because they can outperform many other classifiers when the size of the training set is small, but they are often very complex and some unsatisfactory relevance of results occur frequently. To overcome the above limitations, we propose a SVM relevance feedback CBIR algorithm based on feature reconstruction, in which the covariance matrix based kernel empirical orthogonal complement component analysis is utilized. Firstly, the original input image space is projected nonlinearly onto a high-dimensional feature space by using nonlinear analysis approaches. Secondly, the covariance matrix of the positive feedback images are calculated, and the kernel empirical orthogonal complement components of the covariance matrix are also calculated. Thirdly, the new features of positive feedback images, negative feedback images, and all the remaining images are reconstructed by utilizing the kernel empirical orthogonal complement components of positive feedback images. Finally, a SVM classifier is constructed and all the images are resorted based on the new reconstructed image feature. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches.

LBP와 HSV 컬러 히스토그램을 이용한 내용 기반 영상 검색

본 논문에서는 LBP와 HSV 컬러 히스토그램을 이용한 내용 기반 영상 검색 방법을 제안한다. 영상 검색 시스템에서는 텍스트가 아닌 사용자가 원하는 특정한 객체를 포함하는 영상을 질의로 입력하여 원하는 영상을 검색한다. 대부분의 연구에서는 색상, 질감, 모양 등과 같은 전역 특징 값을 이용하여 영상을 검색한다. 이러한 전역 특징 값들은 하늘이나 바닥과 같은 배경이 큰 부분을 차지하는 영상에서는 특징 값의 대부분이 배경에서 추출되어 영상 검색의 성능 저하를 초래한다. 이러한 문제를 해결하기 위해, 컬러를 이용하여 영상의 배경을 고속으로 검출하고 배경의 영향을 줄여 관심 객체의 특징을 강조한다. 제안된 방법에서는 특징 값으로 HSV 컬러 히스토그램과 Local Binary Patterns을 사용한다. 또한, 색의 경계 부분의 패턴을 추출하기 위해 양자화 된 Hue 채널에서 Local Binary Patterns을 추출한다. 제안된 알고리즘의 성능 검증하기 위해, Corel 1000 database를 이용하여 실험한 결과 82% 이상의 높은 검색 정확도를 나타내었다. In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

Energy Conservation for Image Retrieval on Mobile Systems

Mobile systems such as PDAs and cell phones play an increasing role in handling visual contents such as images. Thousands of images can be stored in a mobile system with the advances in storage technology: this creates the need for better organization and retrieval of these images. Content Based Image Retrieval (CBIR) is a method to retrieve images based on their visual contents. In CBIR, images are compared by matching their numerical representations called features; CBIR is computation and memory intensive and consumes significant amounts of energy. This article examines energy conservation for CBIR on mobile systems. We present three improvements to save energy while performing the computation on the mobile system: selective loading, adaptive loading, and caching features in memory. Using these improvements adaptively reduces the features to be loaded into memory for each search. The reduction is achieved by estimating the difficulty of the search. If the images in the collection are dissimilar, fewer features are sufficient; less computation is performed and energy can be saved. We also consider the effect of consecutive user queries and show how features can be cached in memory to save energy. We implement a CBIR algorithm on an HP iPAQ hw6945 and show that these improvements can save energy and allow CBIR to scale up to 50,000 images on a mobile system. We further investigate if energy can be saved by migrating parts of the computation to a server, called computation offloading . We analyze the impact of the wireless bandwidth, server speed, number of indexed images, and the number of image queries on the energy consumption. Using our scheme, CBIR can be made energy efficient under all conditions.

Introducing New Meta Search Model through Content based Image Retrieval Algorithm

This paper presents an approach to model content based metasearch engine which search all the content related images present in the dataset. Searching through the keywords in an image database require a lot of Meta data (keywords about the image) to be stored for each image in a separate database. This does not lead to an effective search mechanism. This mechanism basically works for the relevant output for the input query image. The process based on the image extraction by the implemented functions like “edge histogram”, “image feature extraction i.e. sharpness, smoothness, color etc.

Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it's Applications

adays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, Relevance Feedback (RF) techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. In this paper Content Based Image Retrieval algorithms using Relevance Feedback technique are discussed. The comparative study of these algorithms is done. This article covers various techniques for implementing Content Based Image Retrieval algorithms , their evaluation parameters used and various possible applications of Content Based Image Retrieval algorithms .

IPixel: A visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

An Interactive System for Computer-Aided Diagnosis of Breast Masses

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists "a visual aid" in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting "abnormalities" similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

An effective similarity measure via genetic algorithm for Content-Based Image Retrieval with extensive features

With the aid of image content, the relevant images can be extracted from the image in the Content Based Image Retrieval (CBIR) system. Concise feature sets limit the retrieval efficiency, to eliminate this shape, colour, texture and contourlet features are extracted. For retrieving relevant images, the optimisation technique Genetic Algorithm (GA) is utilised and for similarity measure Squared Euclidean Distance (SED) is utilised for comparing query image featureset and database image featureset. Hence, from GA based similarity measure, relevant images are retrieved and evaluated by querying different images.

Automated Assessment of Diabetic Retinopathy Severity Using Content-Based Image Retrieval in Multimodal Fundus Photographs

Recent studies on diabetic retinopathy (DR) screening in fundus photographs suggest that disagreements between algorithms and clinicians are now comparable to disagreements among clinicians. The purpose of this study is to (1) determine whether this observation also holds for automated DR severity assessment algorithms, and (2) show the interest of such algorithms in clinical practice. A dataset of 85 consecutive DR examinations (168 eyes, 1176 multimodal eye fundus photographs) was collected at Brest University Hospital (Brest, France). Two clinicians with different experience levels determined DR severity in each eye, according to the International Clinical Diabetic Retinopathy Disease Severity (ICDRS) scale. Based on Cohen's kappa (κ) measurements, the performance of clinicians at assessing DR severity was compared to the performance of state-of-the-art content-based image retrieval (CBIR) algorithms from our group. At assessing DR severity in each patient, intraobserver agreement was κ = 0.769 for the most experienced clinician. Interobserver agreement between clinicians was κ = 0.526. Interobserver agreement between the most experienced clinicians and the most advanced algorithm was κ = 0.592. Besides, the most advanced algorithm was often able to predict agreements and disagreements between clinicians. Automated DR severity assessment algorithms, trained to imitate experienced clinicians, can be used to predict when young clinicians would agree or disagree with their more experienced fellow members. Such algorithms may thus be used in clinical practice to help validate or invalidate their diagnoses. CBIR algorithms, in particular, may also be used for pooling diagnostic knowledge among peers, with applications in training and coordination of clinicians' prescriptions.

CONTENT-BASED IMAGE RETRIEVAL OF CULTURAL HERITAGE SYMBOLS BY INTERACTION OF VISUAL PERSPECTIVES

Content-based Image Retrieval (CBIR) has been an active area of research for retrieving similar images from large repositories, without the prerequisite of manual labeling. Most current CBIR algorithms can faithfully return a list of images that matches the visual perspective of their inventors, who might decide to use a certain combination of image features like edges, colors and textures of regions as well as their spatial distribution during processing. In practice, however, the retrieved images rarely correspond exactly to the results expected by the users, a problem that has come to be known as the semantic gap. In this paper, we propose a novel and extensible multidimensional approach called matrix of visual perspectives as a solution for addressing this semantic gap. Our approach exploits the dynamic cross-interaction (in other words, mix-and-match) of image features and similarity metrics to produce results that attempt to mimic the mental visual picture of the user. Experimental results on retrieving similar Japanese cultural heritage symbols called kamons by a prototype system confirm that the interaction of visual perspectives in the user can be effectively captured and reflected. The benefits of this approach are broader. They can be equally applicable to the development of CBIR systems for other types of images, whether cultural or noncultural, by adapting to different sets of application specific image features.

CBMIR: Content-based image retrieval algorithm for medical image databases

We propose a novel algorithm for the retrieval of images from medical image databases by content. The aim of this article is to present a content-based retrieval algorithm that is robust to scaling, with translation of objects within an image. For the best result and efficient representation and retrieval of medical images, attention is focused on the methodology, and the content of medical images is represented by the regions and relationships between such objects or regions of the Image Attributes (IA) of the objects. The CBMIR employs a new model in which each image is first decomposed into regions. The similarity measurement between images is developed based on a scheme that integrates the properties of all the regions in the images using regional matching. The method can answer queries by example. The efficiency and performance of the presented method has been evaluated using a dataset of about 5,000 simulated, but realistic computed tomography and magnetic resonance images, from which the original images are selected from three large medical image databases. The results of our experiments show more than a 93 percent success rate, which is satisfactory.

Sub‐syllable segment‐based voice conversion using spectral block clustering transformation functions

This paper presents a novel framework for voice conversion based on sub‐syllable spectral block clustering transformation functions. The speech signal is first transferred to a spectrum by Fast Fourier transform. A sonority measure is used to extract sub‐syllable segments from input utterances by computing the energy concentration measure among frequency components. According to the syllable structure of Mandarin, Hidden Markov Model based syllable clustering is used to deal with the variety among different syllables. Dynamic programming is applied to align the spectral blocks of the parallel corpus to constrain the mapping between the spectral unit of the source speaker and that of the listener speaker under the constraint that mapped unities should be constrained to the same sub‐syllable and sub‐band in the Mel‐scale filter bank. A content based image retrieval algorithm is employed to find the target spectral block in the transformation phase. This paper illustrates voice conversion by spectral block transformation that transfers the speech signal of the source speaker to that of the listener. Experimental results show that the proposed method is effective in voice conversion, and the discrimination with regard to speaker identification is better than with traditional approaches. However, there remain additional noises, especially in high frequency components, which reduce the signal quality carried in the transformation phase, due to the fact that speech is not smooth.

Content-based image retrieval algorithm acceleration in a low-cost reconfigurable FPGA cluster

The SMILE project main aim is to build an efficient low-cost cluster based on FPGA boards in order to take advantage of its reconfigurable capabilities. This paper shows the cluster architecture, describing: the SMILE nodes, the high-speed communication network for the nodes and the software environment. Simulating complex applications can be very hard, therefore a SystemC model of the whole system has been designed to simplify this task and provide error-free downloading and execution of the applications in the cluster. The hardware–software co-design process involved in the architecture and SystemC design is presented as well. The SMILE cluster functionality is tested executing a real complex Content-Based Information Retrieval (CBIR) parallel application and the performance of the cluster is compared (time, power and cost) with a traditional cluster approach.