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In order to improve the average recall rate and the average precision rate of image retrieval, an improved fusion algorithm of the weighted features is presented. Firstly,the shape features of images are extracted by using the moment invariant method based on 7 central moments. Meanwhile,the texture features of images are calculated by using the Gray-level Co-occurrence matrix. Then the elements of...
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...
Time-frequency representations of audio signals often resemble texture images. This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images. The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures. While solely based on time-frequency texture features, the algorithm achieves surprisingly...
An evaluation method of human visual impressions in gray scale textures using morphological morphology is proposed. Variations of textures are generated by modifying repetitively arranged objects and configurations of the arrangements of original textures. The variations are presented to human respondents, and similarity of modified textures based on human impressions is evaluated. The results of...
Our goal is to organize the image contents semantically. In this paper, we propose a method to classify the images semantically, using the C-fuzzy algorithm to segment the natural scenes into perceptually uniform regions. The low-level characteristics that are taken into account are: color, texture, shape, absolute spatial arrangement, spatial coherency, and dimension. Since humans are the ultimate...
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