Computing Multi-purpose Image-Based Descriptors for Object Detection: Powerfulness of LBP and Its Variants
In this paper, we present some powerful methods for computing multi-purpose image-based descriptors toward their exploitations in object detection and recognition applications. Image-based descriptors characterize image properties for constituting machine learning-based recognition systems. In this context, we present the principle for computing image-based descriptors using the local binary pattern (LBP) method. Such a method is multi-purpose in the sense that it can be efficiently exploited for the automated recognition of objects of varied natures (e.g., vehicles of traffic-monitoring images, cells of medical images). Then, we propose three variants of LBP, named Mean-LBP, \(\lambda \)-2RLBP, and C2R-LBP. The two latter ones use Hamming distance. Experimental results show that our method can overpass performances of discussed LBP methods notably under realistic conditions of use.
KeywordsObject recognition system Image analysis Machine learning Local binary pattern
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