A binary based HMAX model for object recognition

In this paper, we propose a fast binary based HMAX model (B-HMAX). In our method, we detect corner based interest points after the second layer C1 to extract fewer numbers of features with better distinctiveness, and use binary string to describe the image patches extracted around detected corners,...

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Bibliographic Details
Published in2015 15th International Conference on Control, Automation and Systems (ICCAS) pp. 1297 - 1301
Main Authors Tae-Koo Kang, Huazhen Zhang, Dong-Sung Pae, Myo-Taeg Lim
Format Conference Proceeding
LanguageEnglish
Published Institute of Control, Robotics and Systems - ICROS 01.10.2015
Subjects
Airplanes
Binary descriptor
Biologically inspired model
Biology
Chlorine
Convolution
HMAX
Object recognition
Robustness
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Summary:In this paper, we propose a fast binary based HMAX model (B-HMAX). In our method, we detect corner based interest points after the second layer C1 to extract fewer numbers of features with better distinctiveness, and use binary string to describe the image patches extracted around detected corners, then use hamming distance for matching between two patches in the third layer S2, which is much faster than Euclidean method. Experimental results demonstrate that our proposed B-HMAX model can significantly reduce the total process time, while keeping the accuracy performance as the same with or better than standard HMAX.
ISSN:2093-7121
DOI:10.1109/ICCAS.2015.7364837