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We describe computation methods of Local Binary Pattern (LBP) descriptors as well as of 2-Resolution-level LBP. Both classical and compact versions of these descriptors are illustrated and evaluated with image sets of varied natures. Each considered image set is associated to a contextual study case which has an applicative objective for visual analysis and object categorization. A comparative study shows performances and potentialities of each presented method.

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.