Abstract
Designing efficient and effective keypoint descriptors for an image plays a vital role in many computer vision tasks. The traditional binary descriptors such as local binary pattern and its variants directly perform a binarization operation on the intensity differences of the local affine covariant regions, thus their performance usually drops a lot because of the limited distinctiveness. In this paper, we propose a novel image keypoint descriptor, namely local derivative quantized binary pattern for object recognition. To incorporate the spatial information, we first divide the local affine covariant region into several subregions according to the intensity orders. For each sub region, we quantize the intensity differences between the central pixels and their neighbors in an adaptive way, and then we order the differences and use a hash function to map the differences into binary codes. The binary codes are histogramed to form the feature of each subregion. Furthermore, we utilize multi-scale support regions and pool the histograms together to represent the features of the image. Our approach does not need prior codebook training and hence it is more flexible and easy to be implemented. Moreover, our descriptor can preserve more local brightness and edge information than the traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations and other geometric transformations. Finally we conduct extensive experiments on three challenging datasets (i.e., 53 Objects, ZuBuD, and Kentucky) for object recognition and the experimental results show that our descriptor outperforms the existing state-of-the-art descriptors.
Similar content being viewed by others
References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Alcantarilla, P., Bartoli, A., Davison, A.: KAZE features. In: ECCV, pp. 214–227 (2012)
Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)
Satpathy, A., Jiang, X., Eng, H.L.: Human detection by quadratic classification on subspace of extended histogram of gradients. IEEE Trans. Image Process. 23(1), 287–297 (2014)
Singh, C., Walia, E., Mittal, N.: Robust two-stage face recognition approach using global and local features. Vis. Comput. 28(11), 1085–1098 (2012)
Trzcinski, T., Christoudias, M., Fua, P., Lepetit, V.: Learning image descriptors with the boosting-trick. In: NIPS, pp. 278–286 (2012)
Wu, H., Miao, Z., Wang, Y., Lin, M.: Optimized recognition with few instances based on semantic distance. Vis. Comput. 31(4), 367–375 (2015)
Li, C., Zhou W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1419–1429 (2015)
Vu, N.S., Caplier, A.: Enhanced patterns of oriented magnitudes for face recognition and image matching. IEEE Trans. Image Process. 21(3), 1352–1365 (2012)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Nguyen, D.T., Zong, Z., Li, W., Ogunbona, P.: Object detecton using non-redundant local binary patterns. In: ICIP, pp. 4609–4612 (2010)
Shrivastava, N., Tyagi, V.: An effective scheme for image texture classification based on binary local structure pattern. Vis. Comput. 30(11), 1223–1232 (2014)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: ECCV, pp. 778–792 (2010)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: ICCV, pp. 2564–2571 (2011)
Leutengger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: ICCV, pp. 2548–2555 (2011)
Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retinal keypoint. In: CVPR, pp. 510–517 (2012)
Trzcinski, T., Christoudias, M., Fua, P., Lepetit, V.: Boosting binary keypoint descriptors. In: CVPR, pp. 2874–2881 (2013)
Lin, L., Luo, P., Chen, X., Zeng, K.: Representing and recognizing objects with massive local image patches. Pattern Recognit. 45(1), 231–240 (2012)
Jiang, B., Tang, J., Lin, L.: Robust feature point matching with sparse model. IEEE Trans. Image Process. 23(12), 5175–5186 (2014)
Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit. 48(10), 2993–3003 (2015)
Liu, W., Wang, J., Ji, R., Jiang, Y.-G., Chang, S.-F.: Supervised hasing with kernels. In: CVPR, pp. 2074–2081 (2012)
Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for scalable image retrieval. In: CVPR, pp. 3424–3431 (2010)
Cao, Z., Yin. Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: CVPR, pp. 2707–2714 (2010)
Guo, Z.H., Zhang, L., Zhang, D.: A completed moderning of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)
Hussain, S., Triggs, B.: Visual recognition using local quantized patterns. In: ECCV, pp. 716–729 (2012)
Satpathy, A., Jiang, X., Eng, H.-L.: LBP-based edge-texture features for object recognition. IEEE Trans. Image Process. 23(5), 1953–1964 (2014)
Guo, Z.H., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43(3), 706–719 (2010)
Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)
Zhu, C., Bichot, C.-E., Chen, L.: Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recognit. 46(7), 1949–1963 (2013)
Mu, Y.D., Yan, S.C., Liu, Y., Huang, T., Zhou, B.F.: Discriminative local binary patterns for human detection in personal album. In: CVPR, pp. 1–8 (2008)
Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured HOG-LBP for object localization. In: CVPR, pp. 1393–1400 (2011)
Nguyen, D.T., Ogunbona, P., Li, W.Q.: A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recognit. 46(5), 1485–1500 (2013)
Hu, R.-X., Jia, W., Ling, H., Chao, Y., Gui, J.: Angular patter and binary angular pattern for shape retrieval. IEEE Trans. Image Process. 23(3), 1118–1127 (2014)
Qi, X., Xiao, R., Li, C.-G., Qiao, Y., Guo, J., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2199–2213 (2014)
Lei, Z., Pietikainen, M., Li, S.Z.: Learning discriminant face descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 289–302 (2014)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors—Survey. http://www.robots.ox.ac.uk/~vgg/research/affine/
Acknowledgments
This work is supported partially by Hubei Provincial Natural Science Foundation of China (No.2013CFB152).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shang, J., Chen, C., Pei, X. et al. A novel local derivative quantized binary pattern for object recognition. Vis Comput 33, 221–233 (2017). https://doi.org/10.1007/s00371-015-1179-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-015-1179-7