Sorted Consecutive Local Binary Pattern for Texture Classification

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In this paper, we propose a sorted consecutive local binary pattern (scLBP) for texture classification. Conventional methods encode only patterns whose spatial transitions are not more than two, whereas scLBP encodes patterns regardless of their spatial transition. Conventional methods do not encode patterns on account of rotation-invariant encoding; on the other hand, patterns with more than two spatial transitions have discriminative power. The proposed scLBP encodes all patterns with any number of spatial transitions while maintaining their rotation-invariant nature by sorting the consecutive patterns. In addition, we introduce dictionary learning of scLBP based on kd-tree which separates data with a space partitioning strategy. Since the elements of sorted consecutive patterns lie in different space, it can be generated to a discriminative code with kd-tree. Finally, we present a framework in which scLBPs and the kd-tree can be combined and utilized. The results of experimental evaluation on five texture data sets-Outex, CUReT, UIUC, UMD, and KTH-TIPS2-a-indicate that our proposed framework achieves the best classification rate on the CUReT, UMD, and KTH-TIPS2-a data sets compared with conventional methods. The results additionally indicate that only a marginal difference exists between the best classification rate of conventional methods and that of the proposed framework on the UIUC and Outex data sets.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-07
Language
English
Article Type
Article
Keywords

ROTATION-INVARIANT; FEATURES; REPRESENTATION; SEGMENTATION; FILTERS; SCALE

Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.24, no.7, pp.2254 - 2265

ISSN
1057-7149
DOI
10.1109/TIP.2015.2419081
URI
http://hdl.handle.net/10203/198537
Appears in Collection
CS-Journal Papers(저널논문)
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