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Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry...
Super-peer networks inherit the advantages of P2P networks, such as pooling together the shared data (images in our system) across peers, self-organizing, and fault-tolerance. In addition, they take advantage of the heterogeneity of capabilities across peers in load-balancing and network adaptation. A super-peer node operates as an equal peer, and as a server/parent to a set of peers. Content-based...
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm...
Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel approach for subspace clustering based on Ant Colony Optimisation and its learning mechanism. The proposed...
With the advancement in image capturing device, the image data been generated at high volume. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images...
The content-based image retrieval (CBIR) system aims at searching and browsing the large image digital libraries based on automatically derived imagery features. This paper introduces two algorithms based on the normalized cut for images clustering. We extract the color and texture features for computing the distance between the images, and take advantage of the bipartition method and minimum spanning...
The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Wardpsilas method, with the latter three being different hierarchical methods. The quality of the clusters created by the algorithms was measured in terms of cluster cohesiveness and semantic cohesiveness, and both quantitative and predicate-based similarity criteria were considered.Two...
In content-based image retrieval, how to representation of local properties in an image is one of the most active research issues. In certain circumstance, however, users concern more about objects of their interest and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background). Previous work on represent of local properties normally requires...
In content-based image retrieval (CBIR), similarity measures vary according to the user, and it is difficult to build a retrieval system which reflects the user's similarity measures automatically. Regarding CBIR as consisting of feature extraction, coarse classification and detailed matching stages, this work aims at reflecting the user's similarity measures in coarse classification. After obtaining...
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