Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval
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College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
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College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
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Institute of Informatics and Applications, University of Girona, 17017 Girona, Spain
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School of Software, Jiangxi Normal University, Nanchang 330022, China
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School of Computer Engineering, Weifang University, Weifang 261061, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(2), 315; https://doi.org/10.3390/s19020315
Received: 2 December 2018
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Revised: 11 January 2019
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Accepted: 11 January 2019
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Published: 14 January 2019
(This article belongs to the Special Issue Visual Sensors)
Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.
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MDPI and ACS Style
Feng, Q.; Hao, Q.; Sbert, M.; Yi, Y.; Wei, Y.; Dai, J. Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval. Sensors 2019, 19, 315. https://doi.org/10.3390/s19020315
AMA Style
Feng Q, Hao Q, Sbert M, Yi Y, Wei Y, Dai J. Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval. Sensors. 2019; 19(2):315. https://doi.org/10.3390/s19020315
Chicago/Turabian StyleFeng, Qinghe, Qiaohong Hao, Mateu Sbert, Yugen Yi, Ying Wei, and Jiangyan Dai. 2019. "Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval" Sensors 19, no. 2: 315. https://doi.org/10.3390/s19020315
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