Sorted Consecutive Local Binary Pattern for Texture Classification
Abstract—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.
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