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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

In this paper, a color image retrieval method based on the primitives of images will be proposed. First, the context of each pixel in an image will be defined. Then, the contexts in the image are clustered into several classes based on the algorithm of fast noniterative clustering. The mean of the context in the same class is considered as a primitive of the image. The primitives are used as feature vectors. Since the numbers of primitives between images are different, a specially designed similarity measure is then proposed to do color image retrieval. To better adapt to the preferences of users, a relevance feedback algorithm is provided to automatically determine the weight of each primitive according to the user's response. To demonstrate the effectiveness of the proposed system, several test databases from Corel are used to compare the performances of the proposed system with other methods. The experimental results show that the proposed system is superior to others.

An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

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.

Localization of texts in natural images could be an important stage in many applications such as content-based image retrieval, visual impairment assistance systems, automatic robot navigation in urban environments and tourist assistance systems. However due to the variations of font, script, scale, orientations, color, shadow and lighting conditions, robust scene text localization is still a challenging task. In this paper, we propose a novel method to localize not only Farsi/Arabic and Latin texts with different sizes, fonts and orientations but also low luminance contrast and poor quality ones in the natural images taken with uneven illumination conditions. Firstly, fast weighted median filtering as a nonlinear edge-preserving smoothing filter and then color contrast preserving decolorization are exploited to make the text localization system more robust for low luminance contrast and poor quality texts. In order to extract the Farsi/Arabic and Latin scene texts and also filter the nontext ones, a unified framework is proposed incorporating the maximally stable extremal regions and a novel proposed region detector called Stable Width Stroke Regions which is based on closed boundary regions. Phase congruency and Laplacian operators are exploited to extract the closed boundary regions. Finally, to extract the single text lines, the Meanshift clustering and radon transform were used. Experimental results show that the proposed method localize low luminance contrast and low quality scene texts for both Farsi/Arabic and Latin scripts encouragingly.

In biomedical documents/publications, medical images tend to be complex by nature and often contain several regions that are annotated using arrows. In this context, an automated arrowhead detection is a critical precursor to region-of-interest (ROI) labeling and image content analysis. To detect arrowheads, in this paper, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component (CC) principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic time warping (DTW). The DTW similarity score confirms the presence of arrows in the image. Our test results on biomedical images from imageCLEF 2010 collection shows the interest of the technique, and can be compared with previously reported state-of-the-art results.

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-$M$ retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.

We present a novel technique to separate panels from stitched multipanel figures appearing in biomedical research articles. Since such figures may comprise images from different imaging modalities, separating them is a crucial first step for effective biomedical content-based image retrieval (CBIR): multimodal biomedical document classification and/or retrieval, for instance. The method applies local line segment detection based on the gray-level pixel changes. It then applies a line vectorization process that connects prominent broken lines along the panel boundaries while eliminating insignificant line segments within the panels. We validated our fully automatic technique on a set of stitched multipanel biomedical figures extracted from articles within the Open Access subset of PubMed Central® repository, and achieved precision and recall of 87.16% and 83.51%, respectively, in less than 0.461s per image, on average. We also reported the recent ImageCLEF 2015 competition results that highlight the usefulness of the proposed work.

Content-Based Image Retrieval (CBIR) systems constitute an innovative approach to store, to compare and to query images in a database. Visual aspects such as color, texture or shape are used to perform such operations. Recently, CBIR concepts were applied to build testing oracles for image processing programs, where test verdicts (approval/disapproval) are based on similarity measures between images produced by the program and reference images. However, the results of a CBIR system may vary depending on the components employed in the system (feature extractors and similarity functions), and few studies assessing this influence have been found in the literature. Our aim is to present an empirical analysis of ten similarity functions in CBIR systems within the context of software testing with graphic outputs. A case study with images obtained from a computer-aided diagnosis system in mammography indicated some variability among image test verdicts (approval/disapproval) according to the similarity function choice. The case study also indicates the existence of some clusters of similarity functions with high correlation coefficients.

Aiming at content-based image retrieval (CBIR) in fractal domain, this paper puts forward a fast fractal encoding method to extract image features, which is based on a novel non-searching and adaptive quadtree division. As a result, it enhances fractal coding speed sharply, only needs 0.0485 seconds on average for a 256 × 256 image and is approximately 70 times faster than algorithm in addition to good reconstructed image quality. Furthermore, this paper improves image matching algorithm, consequently enhancing the accuracy of query results. In addition, we present a method to further accelerate image retrieval based on the analysis to fractal codes distance and number. Experimental results show that our proposed method is performs highly in retrieval speed and feasible in retrieval accuracy.

Although image retrieval for e-commerce field has a huge commercial potential, e-commerce oriented content-based image retrieval is still very raw. Modern online shopping systems have certain limitations. In particular, they use conventional tag-based retrieval and lack making use of visual content. The paper presents a methodology to retrieve images of shopping items based on fuzzy dominant colors. People regard color as an aesthetic issue, especially when it comes to choosing the colors of their clothing, apartment design and other objects around. No doubt, color inuences purchasing behavior — to a certain extent, it is a reection of human's likes and dislikes. The fuzzy color model that we are proposing represents the collection of fuzzy sets, providing the conceptual quantization of crisp HSI space having soft boundaries. The proposed method has two parts: assigning a fuzzy colorimetric profile to the image and processing the user query. We also use underlying mechanisms of attention from a theory of visual attention, like perceptual categorization. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level color visual features and high-level concepts are major issues that we plan to tackle in this research.

This paper presents an intelligent method of retrieving images with Chinese captions from an image database. We combine color, shape and spatial features of the image to index and measure the similarity of images. As a technical contribution, a Seed-Filling like algorithm that could extract the shape and spatial relationship feature of images is proposed. Due to the difficulty of determining how far objects separate, we use qualitative spatial relations to analyze object similarities. Also, the system is incorporated with a visual interface and a set of tools, which allows the users to express the query by specifying or sketching the images conveniently. Besides, our feedback learning mechanism enhances the precision of retrieval. Our experience shows that the system is able to retrieve image information efficiently by the proposed approaches.

In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

Human tastes in art motivate the need for effective means to build a visual mosaic picture which is made up of many small tiles. In some previous work researchers have tried to translate pictures' styles.¹ In this paper, a new approach to image processing in arts is presented in the domain of generating a series of artistic mosaic pictures. An arbitrary image is first translated into a mosaic-based one by dividing it into a number of sub-blocks each with the mean value of the pixels in it. Each of these small fragments can be assigned a new location within this image so that a new mosaic picture is generated. By this mean, images with similar color features can be used to create a series of mosaic pictures. The mosaic pictures consist of the same elements as each other, but might be extremely different from the semantic contents. A basic algorithm is presented, followed by some further improvements. Some preliminary experimental results are then given to show the impact of the proposed special techniques.

This article is a comprehensive evaluation of a new framework for indexing image data, called CMVF, which can combine multiple data properties with a hybrid architecture. The goal of this system is to allow straightforward incorporation of multiple visual feature vectors, based on properties such as color, texture and shape, into a single low-dimension vector that is more effective for retrieval than the larger individual feature vectors. Moreover, CMVF is not only constrained to visual properties, but can also incorporate human classification criteria to further strengthen image retrieval process. The controlled study present in this paper concentrates on CMVF's performance on images, examining how the incorporation of extra features into the indexing affects both efficiency and effectiveness. Analysis and empirical evidence suggest that the inclusion of extra visual features can significantly improve system performance. Furthermore, it demonstrated that CMVF's effectiveness is robust against various kinds of common image distortions and initial (random) configuration of neural network.

Relevance feedback, as a user-in-the-loop mechanism, has been widely employed to improve the performance of content-based image retrieval. Generally, in a relevance feedback algorithm, two key components are: (1) how to select a subset of effective features from a large-scale feature pool and, (2) correspondingly, how to construct a suitable dissimilarity measure. In previous work, the biased discriminant analysis (BDA) has been proposed to address these two problems during the feedback iterations. However, BDA encounters the so called small samples size problem because it has a lack of training samples. In this paper, we utilize the generalized singular value decomposition (GSVD) to significantly reduce the small samples size problem in BDA. The developed algorithm is named GSVD for BDA (GBDA). We then kernelize the GBDA to nonlinear kernel feature space. A large amount of experiments were carried out upon a large scale database, which contains 17800 images. From the experimental results, GBDA and its kernelization are demonstrated to outperform the traditional BDA-based relevance feedback approaches and their kernel extensions, respectively.

In statistics, pattern recognition and signal processing, it is of utmost importance to have an effective and efficient distance to measure the similarity between two distributions and sequences. In statistics this is referred to as goodness-of-fit problem. Two leading goodness of fit methods are chi-square and Kolmogorov–Smirnov distances. The strictly localized nature of these two measures hinders their practical utilities in patterns and signals where the sample size is usually small. In view of this problem Rubner and colleagues developed the earth mover's distance (EMD) to allow for cross-bin moves in evaluating the distance between two patterns, which find a broad spectrum of applications. EMD-L1 was later proposed to reduce the time complexity of EMD from super-cubic by one order of magnitude by exploiting the special L1 metric. EMD-hat was developed to turn the global EMD to a localized one by discarding long-distance earth movements. In this work, we introduce a Markov EMD (MEMD) by treating the source and destination nodes absolutely symmetrically. In MEMD, like hat-EMD, the earth is only moved locally as dictated by the degree d of neighborhood system. Nodes that cannot be matched locally is handled by dummy source and destination nodes. By use of this localized network structure, a greedy algorithm that is linear to the degree d and number of nodes is then developed to evaluate the MEMD. Empirical studies on the use of MEMD on deterministic and statistical synthetic sequences and SIFT-based image retrieval suggested encouraging performances.

Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.