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Extraction of sparse features of color images in recognizing objects

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  • Robot and Applications
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Abstract

In this paper, we propose a new object recognition framework that combines Gabor energy filters, a visual cortex model in which single units alternate with complex units, and color information. Each color image is first converted to the CIELAB color space. Rather using Gabor filters in the first layer of the cortex model, to each color component, a set of Gabor energy filters is applied. Thereafter, the superposition responses of the Gabor energy filter outputs over the color components are normalized by divisive normalization. In the fourth layer, sparse features are calculated using a localized pooling method that allows retention of some geometric information from the prototype patches’ positions. Finally, a set of sparse features are exploited by a linear SVM classifier for object recognition and classification. In the learning stage, a set of prototypes is selected randomly over spatial position, spatial size, and several scales simultaneously, and is extracted by the local maximum over scales and orientations, ignoring weaker training scales and orientations. The results of experiments performed on several datasets show that the use of color information in our framework improves object recognition significantly.

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Authors and Affiliations

  1. Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet St. Cau Giay District, Hanoi, Vietnam

    T. T. Quyen Bui & Thang T. Vu

  2. Department of Cogno-Mechatronics Engineering and School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan, 609-735, Korea

    Keum-Shik Hong

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  1. T. T. Quyen Bui
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Correspondence to Keum-Shik Hong.

Additional information

Recommended by Editor Euntai Kim. This work was supported by the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning, Korea (grant no. NRF-2014-R1A2A1A10049727) and Vietnam Academy of Science and Technology, Vietnam (grant no: VAST01.02/14-15).

T. T. Quyen Bui received the B.S. and M.S. degrees in Instrumentation and Control from Hanoi University of Science and Technology, Vietnam, in 2001 and 2006, respectively, and her Ph.D. degree in Mechanical Engineering from Pusan National University, Korea, in 2013. She has joined the Department of Automation Technology, Institute of Information Technology (IOIT), Vietnam Academy of Science and Technology as a researcher since July 2001. At present, she is the head of the Department of Automation Technology, IOIT. Her research interests include computer vision, image processing, robotic system, and measurement systems.

Thang T. Vu received his B.E. and M.S. degrees in Electronics and Telecommunications from the Hanoi University of Science and Technology in 2002 and 2005, respectively. He received his Ph.D. degree in Information System from the Japan Advanced Institute of Science and Technology in 2008. Currently, he is a senior researcher, leader of the Multimedia Human-Machine Language Technology Department at the Institute of Information Technology, Vietnam Academy of Science and Technology. He is also a lecturer at the University of Science and Technology of Hanoi and a member of the Research Institute of Signal Processing. His research interests include language understanding, computer vision, dialog system, and robotics.

Keum-Shik Hong Please see vol. 13, no. 2, p. 425, April, 2015 of this journal.

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Quyen Bui, T.T., Vu, T.T. & Hong, KS. Extraction of sparse features of color images in recognizing objects. Int. J. Control Autom. Syst. 14, 616–627 (2016). https://doi.org/10.1007/s12555-014-0502-9

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