Region-based Image Retrieval System Research Articles

Efficient histogram for region based image retrieval in the discrete cosine transform domain

<p>Recently, several approaches of content based-image retrieval (CBIR), based on the characteristics of discrete cosine transform (DCT), such as decorrelation and concentration of energy in only a few coefficients, have been proposed. To reduce the semantic gap between high level search and low level patterns, a new concept based on region based search region-based image retrieval (RBIR) has been proposed. Recently, we proposed to use shape-adaptive (SA) DCT in a new RBIR system. In this paper, we propose an efficient histogram optimization suited to our region-based concept. This histogram takes into account the pattern’s from the SA-DCT of the border blocks as well as the DCT coefficients of the internal blocks. Our proposed scheme has greatly improved the results compared to region-based reference methods. Regionbased search is limited to the object of interest only, i.e. a region of the query image can only match a region of another image in the database.</p>

Region of interest-based image retrieval techniques: a review

<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

Adaptive learning region importance for region‐based image retrieval

This study addresses the issue of region representation in region-based image retrieval (RBIR). In order to reduce the user's burden of selecting the region of interest, a statistical index called visual region importance (RI) is constructed to describe the region. By learning from user's current and historical feedback information, visual RI can be automatically updated and semantic RI can be obtained. Furthermore, adaptive learning RI and memory learning RI (MLRI) techniques for RBIR system have been presented. Specifically, the MLRI can mitigate the negative influence of interference regions well. Extensive experiments on the Corel-1000 dataset and the Caltech-256 dataset demonstrate that the proposed frameworks are effective, are robust and achieve significantly better performance than the other existing methods.

An integrated approach to region based image retrieval using firefly algorithm and support vector machine

Intelligent image retrieval is a challenging technology in multimedia applications where bridging the gap between user׳s expectation and low level features is typically hard for computing systems. In the proposed approach, a unique method is projected which integrates support vector machine based learning with an evolutionary stochastic algorithm, called firefly algorithm as a relevance feedback approach into a region based image retrieval system. This system overcomes the semantic gap through optimized iterative learning and also provides a better exploration of solution space. Support vector machine learning automatically updates the weights of preferences for relevant images based on the both relevant and irrelevant feedback images. The firefly optimizer guides the swarm agents to move towards the cluster of relevant images in the exploration of the search space based on user׳s feedback. This research study has a focused approach to increase the performance by optimizing region feature with the firefly algorithm. The efficiency of the proposed approach is experimented on the standard subset of Corel, Caltech and Pascal database images. The performance of the proposed approach is compared with other existing retrieval methods like particle swarm optimization, genetic algorithm, support vector machine and query point movement to identify the excellence with regard to the model in terms of precision and recall.

Multi region based image retrieval system

Multimedia information retrieval systems continue to be an active research area in the world of huge and voluminous data. The paramount challenge is to translate or convert a visual query from a human and find similar images or videos in large digital collection. In this paper, a technique of region based image retrieval, a branch of Content Based Image Retrieval, is proposed. The proposed model does not need prior knowledge or full semantic understanding of image content. It identifies significant regions in an image based on feature-based attention model which mimic viewer’s attention. The Curvelet Transform in combination with colour descriptors are used to represent each significant region in an image. Experimental results are analysed and compared with the state-of-the-art Region Based Image Retrieval Technique.

Decomposition of color images into singular components

У цій статті запропоновано сингулярне розкладання кольорових зображень на адитивні компоненти. Дослідження отриманих адитивних компонент на множині різних зображень показали, що кількість крупномасштабних сегментів на зображенні можна встановити, проаналізувавши компоненти власних векторів, які отримані у результаті сингулярного розкладання

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods. 

Semantic real-world image classification for image retrieval with fuzzy-ART neural network

Image retrieval based on semantic learning has attracted considerable attention in recent years. Analyzing the contents of an image and retrieving corresponding semantics are important in semantic-based image retrieval systems. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. In this paper, we apply principal component analysis to extract significant region features and then incorporate them into the proposed two-phase fuzzy adaptive resonance theory neural network (Fuzzy-ARTNN) for real-world image content classification. In general, Fuzzy-ARTNN is an unsupervised classifier. The training patterns in image content analysis are labeled with corresponding categories. This category information is useful for supervised learning. Thus, a supervised learning mechanism is added to label the category of the cluster centers derived by the Fuzzy-ARTNN. Moreover, based on the content analysis by the proposed two-phase Fuzzy-ART, each region in an image is associated with a high-level semantic concept. The proposed system supports both query by keyword(s) with/without region size and query by specified region(s). Experimental results show that the proposed method has high accuracy for semantic-based photograph content analysis and that the results of photograph content analysis are similar to the perception of human eyes. In addition, the semantic-based image retrieval system has a high retrieval rate.

Object-based semi-automatic tool for content retrieval

The essence of a content-based retrieval system is to find images that are semantically related to the user's query from a database. Humans are much better than computers at extracting semantic information from images, being able to segment individual objects subconsciously. The availability of a pool of pre-segmented objects found using region analysis allows the human behaviour to be replicated. A semi-automatic tool for a region-based image retrieval system is presented.

Region-based image retrieval system with heuristic pre-clustering relevance feedback

Relevance feedback (RF) and region-based image retrieval (RBIR) are two widely used methods to enhance the performance of content-based image retrieval (CBIR) systems. In this paper, these two methods are combined to have the promising result of CBIR. Rather than using a single positive feedback group, the proposed approach embeds RF in the RBIR scheme using multiple positive and negative groups. To guide users in grouping the positive feedbacks, the proposed system provides an objectively heuristic pre-clustering result automatically. Referring to these guiding clusters, the users can then easily and subjectively re-group the positive feedbacks in accordance with his/her particular interests. A region-weighting scheme reflecting the process of human visual perception is proposed to enhance the weighting importance assigned to the region whose pixels are closer to the attention center. Finally, a modified Group Biased Discriminant Analysis (GBDA) is developed and applied to the similarity measure between images constructed on the basis of the region-based relevance feedbacks.

Semantic analysis of real-world images using support vector machine

Digital cameras and thus digital images are now ubiquitous. How to efficiently manage a large amount of images has become important. The semantic analysis of images is an important issue in multimedia processing. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. Recently, the support vector machine (SVM) has been proposed to solve the classification problem. It can generate a hyperplane to separate two sets of features and provides good generalization performance. In this paper, we propose a novel method which integrates principal component analysis (PCA) and SVM neural networks for analyzing the semantic content of natural images, in which principal component analysis (PCA) is applied to reduce the dimension of features. Experimental results show that the proposed method is capable of analyzing the components of photographs into semantic categories with high accuracy, resulting in photographic analysis that is similar to human perception. The performance of the proposed method is better than that of the traditional radial basis function (RBF) neural network.

Region-based image retrieval using color-size features of watershed regions

This paper presents a region-based image retrieval system that provides a user interface for helping to specify the watershed regions of interest within a query image. We first propose a new type of visual features, called color-size feature, which includes color-size histogram and moments, to integrate color and region-size information of watershed regions. Next, we design a scheme of region filtering that is based on color-size histogram to fast screen out some of most irrelevant regions and images for the preprocessing of the image retrieval. Our region-based image retrieval system applies the Earth Mover’s Distance in the design of the similarity measure for image ranking and matching. Finally, we present some experiments for the color-size feature, region filtering, and retrieval results that demonstrate the efficiency of our proposed system.

Region-based image retrieval with high-level semantics using decision tree learning

Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key features of the system are: (1) it supports both query by keyword and query by region of interest. The system segments an image into different regions and extracts low-level features of each region. From these features, high-level concepts are obtained using a proposed decision tree-based learning algorithm named DT-ST. During retrieval, a set of images whose semantic concept matches the query is returned. Experiments on a standard real-world image database confirm that the proposed system significantly improves the retrieval performance, compared with a conventional content-based image retrieval system. (2) The proposed decision tree induction method DT-ST for image semantic learning is different from other decision tree induction algorithms in that it makes use of the semantic templates to discretize continuous-valued region features and avoids the difficult image feature discretization problem. Furthermore, it introduces a hybrid tree simplification method to handle the noise and tree fragmentation problems, thereby improving the classification performance of the tree. Experimental results indicate that DT-ST outperforms two well-established decision tree induction algorithms ID3 and C4.5 in image semantic learning.

Similarity-based online feature selection in content-based image retrieval

Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time.

Region-based image retrieval: fast coarse segmentation and fine color description

The two major problems raised by a region-based image retrieval system are the automatic detection and visual description of regions. We adopt a coarse detection and fine description approach. In this paper we first present a new method of unsupervised coarse detection which provides intuitive and visually characteristic regions of interest. This segmentation scheme is based on the classification of local distributions of quantized colors (LDQC). The Competitive Agglomeration classification algorithm is used which has the advantage to automatically determine the number of classes. Then, considering that description must be finer for regions than for images, we propose a new region descriptor of fine color variability: the adaptive distribution of color shades (ADCS). Combined with an appropriate similarity measure, the high color resolution of ADCS improves the perceptual similarity of retrieved regions compared to existing color descriptors.

Region-based Image Retrieval Using Probabilistic Feature Relevance Learning

Region-Based Image Retrieval (RBIR), a specialisation of content-based image retrieval, is a promising and important research area. RBIR usually requires good segmentation, which is often difficult to achieve in practice for several reasons, such as varying environmental conditions and occlusion. It is, therefore, imperative to develop effective mechanisms for interactive, region-based visual query in order to provide confident retrieval performance. In this paper, we present a novel RBIR system, Finding Region In the Pictures (FRIP), that uses human-centric relevance feedback to create similarity metric on-the-fly in order to overcome some of the limitations associated with RBIR systems. We use features such as colour, texture, normalised area, shape and location, extracted from each region of a segmented image, to represent image content. For each given query, we estimate local feature relevance using probabilistic relevance model, from which to create a flexible metric that is highly adaptive to query location. As a result, local data densities can be sufficiently exploited, whereby rapid performance improvement can be achieved. The efficacy of our method is validated and compared against other competing techniques using real world image data.