Abstract
Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) descriptor for recognizing scene categories, is presented in this paper. We show that scene categorization, especially for indoor and outdoor environments, requires its visual descriptor to process properties that are different from other vision domains (e.g., SIFT descriptor used for object categorization). Our proposed PH-MBLBP satisfies these properties and suits the scene categorization task. Since the proposed PH-MBLBP mainly encodes micro- and macro-structures of image patterns, thus, it provides relatively more complete image descriptor than the basic LBP operator. Moreover, our PH-MBLBP descriptor is more powerful texture descriptor than the conventional operator and it can also be calculated extremely fast. Our experiments demonstrate that PH-MBLBP outperforms the other descriptor such as SIFT.
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