Title :
Image retrieval based on 72-trees and genetic algorithm
Author :
Liang Lei ; Jun Peng ; Bo Yang
Author_Institution :
Coll. of Electr. & Inf. Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China
Abstract :
Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. However, how to quickly retrieving images is a challenge because that the speed and efficiency of retrieving image from Internet image is most important. We used genetic algorithm to improve the method based on HSV color space, and optimized the computational workload. First, the paper introduces how to extract dominant color of an image based on HSV color space. Then, it describes how to use genetic algorithm to optimize the algorithm of extracting dominant color. In the end, genetic algorithm is be used for the similarity measure of images. The experiments and results, which based on Corel database, showed that this method has greatly improved the image retrieval in time and precision rates.
Keywords :
content-based retrieval; genetic algorithms; image colour analysis; image retrieval; image texture; tree data structures; 72-trees; Corel database; HSV color space; Internet image; computational workload optimization; content-based image retrieval systems; dominant image color extraction; genetic algorithm; image descriptors; image retrieval improvement; image shape information; image texture; precision rate; similarity measure; time rate; Feature extraction; Genetic algorithms; Image color analysis; Image retrieval; Indexes; Instruction sets; HSV color space; dominant color; genetic algorithm; image retrieval;
Conference_Titel :
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2013 12th IEEE International Conference on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4799-0781-6
DOI :
10.1109/ICCI-CC.2013.6622271