NDLI: Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval

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Cognitive Computation : Volume 8, Issue 4, August 2016

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Cognitive Computation : Volume 1

Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval

Content Provider	Springer Nature Link
Author	Zhou, Yan Zeng, Fan Zhi Zhao, Hui min Murray, Paul Ren, Jinchang
Copyright Year	2016
Abstract	Although content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades, there are still challenging problems in properly understanding the process in feature extraction and image matching. Consequently, significant research is still required to develop solutions for practical applications, especially in exploring and making the best using of the cognitive aspects of the human vision system.Motivated by three cognitive properties of human vision, namely hierarchical structuring, color perception and embedded compressed sensing, we proposed a novel framework for CBIR. First, we use a hierarchical approach to perform discrete cubic partitioning of the image in the HSV space. Then, we propose a new hierarchical mapping of the image data through the use of hierarchical operators: SGLCM. These features are then integrated in a 2D CS model, which extracts refined features and suppresses noise. Finally, the resultant features are used for similarity-based ranking to perform CBIR.Experiments were performed using two Corel image datasets, i.e., the Corel-1000 dataset which contains 1000 images in 10 image categories and the Corel-10000 dataset which contains 10000 images in 100 image categories where each category contains 100 images. In comparison with three other state-of-the-art approaches, the proposed method has demonstrated much improved retrieval accuracy, especially for images with rich color contents and detail, yet the computational complexity has been significantly reduced to meet the needs for real-time online applications. The implication of the study is that the exploitation of cognitive properties of our human vision systems in effective CBIR. Future research work can be further explored to address some limitations for optimized parameter setting, adaptive feature fusion and improved machine learning.
Starting Page	877
Ending Page	889
Page Count	13
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	5
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-08-08
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hierarchical visual perception Two-dimensional compressive sensing (2D CS) Content-based image retrieval (CBIR) Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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