Image retrieval using both color and texture features

This paper has a further exploration and study of visual feature extraction. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimension feature...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 2228 - 2232
Main Author Fan-Hui Kong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
CCM
Content based retrieval
Cybernetics
Equations
Feature extraction
GLCM
Humans
Image color analysis
Image retrieval
Machine learning
Quantization
Space technology
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Summary:This paper has a further exploration and study of visual feature extraction. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture feature extraction is obtained by using gray-level co-occurrence matrix (GLCM) or color co-occurrence matrix (CCM). Through the quantification of HSV color space, we combine color features and GLCM as well as CCM separately. Depending on the former, image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. Through the image retrieval experiment, indicate that the use of color features and texture based on CCM has obvious advantage.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212186