Abstract
Matching keypoints across images is the base of numerous Computer Vision applications, which is often done with local feature descriptors. Hand-crafted descriptors such as SIFT and SURF are still established leaders in the field since they are discriminative as well as robust.
In this paper, we introduce a novel COGE descriptor, a simple yet effective method for keypoint description. By exploiting the anisotropy and the non-uniformity of the underlying gradient distributions, the proposed COGE is highly discriminative and robust. In addition, COGE contains only 480/240/120 bits and can be matched by using Hamming distance, making it ideal for mobile applications. To evaluate the performance of COGE, a comprehensive comparison against SIFT, SURF, ORB and BRISK is performed on three benchmark datasets: the dataset of Mikolajczyk, the INRIA Holidays and the UKbench. Experimental results show that our proposed COGE descriptor significantly outperforms existing schemes.
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Mao, Z., Zhang, Y., Tian, Q. (2013). COGE: A Novel Binary Feature Descriptor Exploring Anisotropy and Non-uniformity. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_34
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DOI: https://doi.org/10.1007/978-3-319-03731-8_34
Publisher Name: Springer, Cham
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