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Local features and histogram based planar object recognition

In this paper, we propose an approach for planar object recognition using binary local invariant feature and local histogram features, in scene image. First, the object is detected based on a homography which represents transformation of object in scene image from reference image, estimated from matching pairs of keypoints between two images. Then, local intensity histograms are computed from blocks inside scene object. In order to locate these blocks, a reference object is divided into many blocks then the corresponding blocks of the scene object are located based on the homography with assumption that object is a plane. To make the feature invariant to the illumination, the local histogram is computed from only intensity component (V) of HSV color of image. Similarity of two images is calculated from the similarity of their local histogram features and the matching ratio. The recognized object is the most similar reference object in data set. For evaluation, we experiment our method with a planar dataset from Stanford University which includes book cover, cd cover, painting, business card, etc. According to the result, our proposed method gets the higher accuracy compare to some other methods while remaining significant processing for an online application.