Zhou, Yan and Zeng, Fan-Zhi and Zhao, Hui-min and Murray, Paul and Ren, Jinchang (2016) Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval. Cognitive Computation. ISSN 1866-9964
Zhou_etal_CC2016_ffective_content_based_color_image_retrieval.pdf - Accepted Author Manuscript
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Content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades. While the field is relatively mature, significant research is still required in this area to develop solutions for practical applications. One reason that practical solutions have not yet been realized could be due to a limited understanding of the cognitive aspects of the human vision system. Inspired by three cognitive properties of human vision, namely, hierarchical structuring, color perception and embedded compressive sensing, a new CBIR approach is proposed. In the proposed approach, the Hue, Saturation and Value (HSV) color model and the Similar Gray Level Co-occurrence Matrix (SGLCM) texture descriptors are used to generate elementary features. These features then form a hierarchical representation of the data to which a two-dimensional compressive sensing (2D CS) feature mining algorithm is applied. Finally, a weighted feature matching method is used to perform image retrieval. We present a comprehensive set of results of applying our proposed Hierarchical Visual Perception Enabled 2D CS approach using publicly available datasets and demonstrate the efficacy of our techniques when compared with other recently published, state-of-the-art approaches.
|Notes:||The final publication is available at Springer via http://dx.doi.org/10.1007/s12559-016-9424-6|
|Keywords:||hierarchical visual perception, two-dimensional compressive sensing, content-based image retrieval, image matching, human vision, cognitive abilities, hierarchical structuring, color perception, colour perception, embedded compressed sensing, Electronic computers. Computer science, Computer Science Applications, Computer Vision and Pattern Recognition|
|Subjects:||Science > Mathematics > Electronic computers. Computer science|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
|Depositing user:||Pure Administrator|
|Date Deposited:||12 Aug 2016 10:57|
|Last modified:||23 Apr 2017 01:29|