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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...
An approach to search images from a databases by structural features got from the three stages clustering algorithm is considered. Image key forming investigation for visual pattern processing is presented.
An approach to search images from a databases by structural features got from the three stages clustering algorithm is considered. Software packages architecture for visual pattern processing is presented.
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...
In the recently proposed latent perceptual indexing of audio, a collection of clips is indexed using unit-document frequency measures between a set of reference clusters as units and the clips as the documents. The reference units are derived by clustering the bag-of-feature vectors extracted from the whole audio library using an unsupervised clustering technique. Indexing is achieved through reduced-rank...
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...
Inspired by the success of inverted indexing in the textual search domain, we provide sparseness justifications for using inverted file indexing on image content, which paves the way for developing scalable image content search systems. We use clustering to automatically generate a content vocabulary. To avoid the problem of generating cluster centers that are overcrowded in high density areas for...
The performance of content-based image retrieval systems has proved to be inherently constrained by the used low-level features, and cannot give satisfactory results when the user's high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately,...
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