Local Intensity Ordering based Binary Patterns for Image Region Description
DOI:
https://doi.org/10.14738/aivp.53.3279Keywords:
Image region descriptor, image feature matching, local binary pattern, local intensity ordering, object recognition, texture classificationAbstract
Local image region description is a fundamental task for image feature matching in the field of Computer Vision. A good image region descriptor should have the ability to discriminate image features even though the images differ due to photometric variations and geometric transformations. Over these years, many local region descriptors have been proposed to tackle the aforementioned challenges. Achieving rotation invariance in keypoint description is considered one of the main challenges in local region description and matching. Previous approaches proposed to tackle rotation variations depend on unreliable dominant orientation estimation. In this paper, two novel local image region descriptors called Local Intensity Order-based Center Symmetric Local Binary Patterns (LIOCSLBP) and Local Intensity Order-based Orthogonally Combined Local Binary Patterns (LIOOCLBP) are proposed to build rotation invariant local region descriptions. The rotation invariance characteristic of the proposed binary pattern-based local region description is achieved by applying a simple and efficient mechanism called Local Intensity Ordering (LIO). The proposed descriptors use double interest regions for each interest point to improve feature discrimination. In order to further improve the feature discrimination ability RGBLIOCSLBP, RGBLIOOCLBP, HSVLIOCSLBP and HSVLIOOCLBP are also proposed exploiting RGB and HSV color models. Extensive experiments are conducted to evaluate the performance of the proposed descriptors on standard benchmark datasets for image matching, object recognition and scene recognition against the state-of-the-art descriptors. The experimental results show that the proposed descriptors are highly competitive to several state-of-the-art local region descriptors where the proposed descriptors outperformed the comparative approaches in many cases.
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