LBP-Based Edge-Texture Features for Object Recognition

LBP-Based Edge-Texture Features for Object Recognition
Title:
LBP-Based Edge-Texture Features for Object Recognition
Other Titles:
IEEE Transactions on Image Processing
Publication Date:
11 March 2014
Citation:
Satpathy, A.; Jiang, X.; Eng, H., "LBP-Based Edge-Texture Features for Object Recognition," Image Processing, IEEE Transactions on , vol.23, no.5, pp.1953,1964, May 2014 doi: 10.1109/TIP.2014.2310123
Abstract:
This paper proposes two sets of novel edge-texture features, Discriminative Robust Local Binary Pattern (DRLBP) and Ternary Pattern (DRLTP), for object recognition. By investigating the limitations of Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features. They solve the problem of discrimination between a bright object against a dark background and vice-versa inherent in LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes and their complements in the same block are mapped to the same code. Furthermore, the proposed features retain contrast information necessary for proper representation of object contours that LBP, LTP, and RLBP discard. Our proposed features are tested on seven challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech 256, Brodatz, and KTH-TIPS2-a. Results demonstrate that the proposed features outperform the compared approaches on most data sets.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
1057-7149
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