Region-based Image Retrieval Research Articles

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.

Texture classification and discrimination for region-based image retrieval

In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification. Moreover, shape irregularity can be a problem since Gabor and curvelet transforms can only be applied on the regular shapes. In this paper, we propose an approach that uses both the Gabor wavelet and the curvelet transforms on the transferred regular shapes of the image regions. We also apply a fitting method to encode the sub-bands’ information in the polynomial coefficients to create a texture feature vector with the maximum power of discrimination. Experiments on texture classification task with ImageCLEF and Outex databases demonstrate the effectiveness of the proposed approach.

Significant region-based image retrieval

In the era of multimedia technologies, the need for information/data retrieval systems getting more attention. The data might be image, video, audio and/or text files. Digital libraries, surveillance application, web applications and many other applications that handle huge volume of data essentially have data retrieval components. In this paper, a technique of region-based image retrieval (RBIR), a branch of content-based image retrieval (CBIR), is proposed. The proposed model identifies a significant region in an image using visual attention-based mechanism and represents them using its color layout descriptors and curvelet descriptors. These features are extracted from significant region of query image and images in the database. The likeness between the query region and database image region is ranked according to a similarity measure computed from the feature vectors. The proposed model does not need a full semantic understanding of image content, uses visual metrics such as proximity, size, color contrast and nearness to image’s boundaries to locate viewer’s attention and uses curvelet transform in combination with color layout descriptor to represent the significant region. Experimental results are analyzed and compared with the state-of-the-art CBIR Systems and RBIR Technique.

OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval

Content Based Image Retrieval (CBIR) has been the most significant area in the applications of Pattern recognition and Computer Vision for the last three decades. However, there are many open problems left unresolved. Among these, one of the current problems of CBIR is to obtain an effective Similarity Measure. The CBIR systems make use of Integrated Region Matching (IRM) to match segmented images which is computationally economic, but it is not a metric distance whereas systems that use Minimum Cost Region Matching (MiCRoM) as a similarity measure is a metric distance, but computationally expensive. In order to address the above problem, this paper has developed the Optimal Cost Region Matching (OCRM) similarity measure for region based image retrieval. The proposed OCRM uses the north-west corner rule of the Transportation problem that fulfills the monge property. The experiment carried out on 1000 color images taken from the Corel database that are compared with IRM, and MiCRoM similarity measures.

Adaptive region matching for region‐based image retrieval by constructing region importance index

This study deals with the problem of similarity matching in region‐based image retrieval (RBIR). A novel visual similarity measurement called adaptive region matching (ARM) has been developed. For decreasing negative influence of interference regions and important information loss simultaneously, a region importance index is constructed and semantic meaningful region (SMR) is introduced. Moreover, ARM automatically performs SMR‐to‐image matching or image‐to‐image matching. Extensive experiments on Corel‐1000, Caltech‐256 and University of Washington (UW) databases demonstrate the authors proposed ARM is more flexible and more efficient than the existing visual similarity measurements that were originally developed for RBIR.

The Initial Retrieval Based on Image Segmentation

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

The Initial Retrieval Based on Image Segmentation

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

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.

Gaze-Based Relevance Feedback for Realizing Region-Based Image Retrieval

In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user's relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of region-based RF approaches, i.e., the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.

Multi-Resolution Salient Region Detection in Frequency Domain

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

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

Efficient Image Retireval Using Region Based Image Retrieval

Early image retrieval techniques were based on textual annotation of images. Annotating images manually is a cumbersome and expensive task for large image databases, and is often subjective, context-sensitive and incomplete. Content based image retrieval, uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. The Region Based Image Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a k-means clustering algorithm to segment an image into regions. Each region is represented by means of a set of features and the similarity between regions is measured using a specific metric function on such features.

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. 

Analysis of Spatial Features in CBIR System

Content based image retrieval from large database has become an area of wide interest nowadays in many applications. Content-based image retrieval (CBIR) technique use image content to search and retrieve digital images. Content-based image retrieval (CBIR) is an important research area for manipulating large amount of image databases. In this paper the analysis work is done for finding the spatial features and collects them into a frame to view all the spatial features and the scope of implementing these features into the image retrieval. The commercial image search engines available as on date are: QBIC, VisualSeek, Virage, Netra, PicSOM, FIRE, AltaVista, etc. Region-Based Image Retrieval (RBIR) is a promising extension of CBIR. The shape and spatial features are quite simple to derive and effective, and can be extracted in real time. Our analysis is able to propose a system that has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. General Terms CBIR, Spatial features, Feature Extraction.

REGION-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK

Region-based image retrieval (RBIR), a special type of content based image retrieval (CBIR), is an important research. This paper presents integration of RBIR with relevance feedback (RF) to enhance the performance of CBIR. Watershed algorithm is used to extract regions but not all regions are with the same importance. So, a region-weighting scheme reflecting the process of human visual perception is proposed. By using relevance feedback method, the matching process could improve retrieval performance interactively and allow progressive refinement of query results according to the user's feedback action.

Region-based image retrieval using the semantic cluster matrix and adaptive learning

An image retrieval system, which reduces the semantic gap between the low level image features and the high level semantic concepts, using the semantic cluster matrix (SCM) and adaptive learning during testing, is proposed. This mechanism retrieves semantically relevant images for both trained and untrained image categories. The SCM groups the new categories into semantic clusters, records the cluster's semantic information, gets the relevance feedback (RF) from the user, and records this in the SCM. Thus, the SCM adaptively learns about the new categories during the testing time, and is able to retrieve semantically relevant images for untrained categories also. Experiments were conducted using the Caltech image dataset consisting of 101 categories of images. The obtained results demonstrate that the proposed approach achieves good performance in terms of retrieval accuracy.

Rotation Invariant Curvelet Features for Region Based Image Retrieval

There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.

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.

SRBIR: Semantic Region Based Image Retrieval by Extracting the Dominant Region and Semantic Learning

Problem statement: The Semantic Region Based Image Retrieval (SRBIR) system that automatically segments the dominant foreground region, consisting of the semantic concept of the image, such as elephants, roses and does the semantic learning, is proposed. Approach: The system segments an image into different regions and finds the dominant foreground region in it, which is the semantic concept of that image. Then it extracts the low-level features of that dominant foreground region. The Support Vector Machine-Binary Decision Tree (SVM-BDT) is used for semantic learning and it finds the semantic category of an image. The low level features of the dominant region of each category image are used to find the semantic template of that category. The SVM-BDT is constructed with the help of these semantic templates. The high level concept of the query image is obtained using this SVM-BDT. Similarity matching is done between the query image and the set of images belonging to the semantic category of the query image and the top images with least distances are retrieved. Results: Experiments were conducted using the COREL dataset consisting of 10,000 images and its subset with 1000 images of 10 different semantic categories. The obtained results demonstrate the effectiveness of the proposed framework, compared to those of the commonly used region based image retrieval approaches. Conclusion: Efficient image searching, browsing and retrieval are required by users from various domains, such as medicine, fashion, architecture, training and teaching. The proposed SRBIR system aims at retrieving images based on their semantic content by extracting the dominant foreground region in the image and learning its semantic concept with the help of the SVM-BDT. The proposed SRBIR system provides an efficient image search based on semantics, with high accuracy and less access time.

Effective image retrieval techniques based on novel salient region segmentation and relevance feedback

This paper proposes an effective region-based image retrieval technique based on novel salient region segmentation and relevance feedback. With a good and fast segmentation technique, our system achieves an on-the-fly segmentation capability, which enables users to select particular regions for matching and feedbacks without waiting for image segmentation. Therefore, we adopt a relatively simple feedback schemes to derive the intent of the user. The experimental results show that the system performance is greatly improved with this capability. Furthermore, a Quick-match algorithm is also presented in this paper. The mechanism of the Quick-match algorithm is to exclude from distance computation regions that are of low possibility to be the top-Mmatches. This algorithm excludes most of regions from distance computation and therefore greatly cuts down the turnaround time of the retrieval with slightly degradation of precision.