Design and evaluation of a content-based image retrieval system
Design and management of multimedia information systems
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
Multishape-features and text-feature integration on 3D model similarity retrieval
International Journal of Innovative Computing and Applications
Edge detection in digital images using fuzzy numbers
International Journal of Innovative Computing and Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Content-based image retrieval using a combination of visual features and eye tracking data
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Content-based image retrieval by combining genetic algorithm and support vector machine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Directionally adaptive single frame image super resolution
International Journal of Innovative Computing and Applications
IEEE Transactions on Circuits and Systems for Video Technology
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If we want the computer to behave like humans, especially when they are recognising an image, we must study the human vision system carefully. Obviously, the colour and the texture are the most important perceptual characteristics of humans. When someone retrieval an image, he or she can use a variant of colour spaces, which have different advantages. In this paper, we replace the original colour space with HSV in MPEG-7 colour layout descriptor according to the nature of the human vision system. Experiment shows that the modified method increased retrieval efficiency greatly. Further more, we establish a multi-feature space, both homogeneous texture descriptor and colour layout descriptor are used. Since there are differences in human perceptions of colour and texture, in order to successfully retrieval an image which caters to the users, parallel genetic algorithm PGA is employed to adjust the weight of each feature space. The experimental evidence shows that the method made the computer behave like human.