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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References
Alahi, A., Ortiz, R., et al.: Freak: Fast retina keypoint. In: CVPR (2012)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Chandrasekhar, V., Takacs, G.: et al. Compressed Histogram of Gradients: A Low-Bitrate Descriptor. IJCV (2012)
Heinly, J., Dunn, E., Frahm, J.-M.: Comparative Evaluation of Binary Features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 759–773. Springer, Heidelberg (2012)
Hui Tang, J., Cheng Yan, S., Chang Hong, R., Qi, G.-J., Chua, T.-S.: Inferring Semantic Concepts from Community-Contributed Images and Noisy Tags. ACM Multimedia (2009)
Hui Tang, J., Jie Li, H., Qi, G.-J., Chua, T.-S.: Image Annotation by Graph-based Inference with Integrated Multiple/Single Instance Representations. In: TMM (2010)
Jegou, H., Douze, M., Schmid, C.: Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)
Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. In: CVPR (2004)
Leutenegger, S., et al.: Brisk: Binary robust invariant scalable keypoints. In: ICCV (2011)
Lowe, D.: Distinctive image features from scale-invariant keypoints. In: IJCV (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI (2005)
Nistr, D., Stewnius, H.: Scalable Recognition with a Vocabulary Tree. In: CVPR (2006)
Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Rublee, E., Rabaud, V., et al.: Orb: an efficient alternative to sift or surf. In: ICCV (2011)
Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. PAMI (2010)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Foundations and Trends® in Computer Graphics and Vision 3(3), 177–280 (2008)
Wang, M., Hua, X.-S., Hong, R.-C., Tang, J.-H., Qi, G.-J., Song, Y.: Unified Video Annotation Via Multi-Graph Learning. TCSVT (2009)
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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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