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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

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Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval

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

[img] Text (Zhou-etal-CC2016-ffective-content-based-color-image-retrieval)
Zhou_etal_CC2016_ffective_content_based_color_image_retrieval.pdf - Accepted Author Manuscript
Restricted to Repository staff only until 8 August 2017.

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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.