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In order to solve the problem of millions image retrieval in science and technology resources database, the authors firstly studied extraction technologies of bottom image features in CBIR. Sub-block histogram method was used in the color feature extraction, and the Gabor wavelet transformation method were used in the texture feature extraction, while the invariant moment method was used in the shape...
The energy moments based on Discrete Cosine Transform are presented as an efficient feature descriptor. Based on this, a new approach for image retrieval using integrated annular color moments and energy moments features is proposed. The experimental results show that the proposed method is not only very robust in terms of scale and rotation invariance but also efficient.
This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. Single feature describes image content only from one point of view, which has a certain one-sided. Fusing multi-feature similarity score is expected to improve the system's retrieval performance. In this paper, the retrieval results from color feature and texture feature are analyzed,...
An image retrieval system is presented, which used HSV color space and wavelet transform approach for feature extraction. Firstly, we quantified the color space in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the work of texture feature extraction is obtained by using wavelet. Finally, we combine color feature and texture feature...
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...
Making full use of image information with its own to extract features is the crucial problem in content based image retrieval (CBIR). In this paper, quotient space(QS) granularity computing theory is imported into image retrieval field, granularity thinking in image retrieval is clarified, and a novel image retrieval method is proposed. Firstly, aiming at the different behaviors under different granularities,...
A number of approaches have been used recently for image retrieval using color features. Use of orthogonal moments instead of normal moments and histograms has been found to be more effective. We propose a scheme to further improve the efficiency by using the most differentiating moments heuristically. The performance for moments calculated using four different orthogonal polynomials is compared and...
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