Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification |
Xu, Mengxi
(School of Computer Science and Technology, Nanjing University of Science & Technology)
Sun, Quansen (School of Computer Science and Technology, Nanjing University of Science & Technology) Lu, Yingshu (College of Computer and Information, Hohai University) Shen, Chenming (School of Computer Engineering, Nanjing Institute of Technology) |
1 | W. Tao, Y. Zhou, L. Liu, et al., “Spatial adjacent bag of features with multiple super pixels for object segmentation and classification”. Information Sciences, 281: 373-385, 2014. DOI ScienceOn |
2 | A. Shi, L. Xu, F. Xu, et al., “Multispectral and panchromatic image fusion based on improved bilateral filter,” Journal of Applied Remote Sensing, 5(1): 053542-1-053542-17, 2011. DOI |
3 | F. Xu, T. Fan, C. Huang, et al., “Block-Based MAP Super-resolution Using Feature-Driven Prior Model,” Mathematical Problems in Engineering, 48(1):331-350, 2014. |
4 | J. Wang, J. Yang, K. Yu, et al., “Locality-constrained linear coding for image classification,” Computer Vision Pattern Recognition, 3360-3367, 2010. |
5 | M. Zhang, A. A. Sawchuk, “Motion primitive-based human activity recognition using a bag-of-features approach,” Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 631-640, 2012. |
6 | L. Zhou, Z. Zhou, D. Hu, “Scene classification using a multi-resolution bag-of-features model,” Pattern Recognition, 46(1): 424-433, 2013. DOI ScienceOn |
7 | S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Computer Vision Pattern Recognition, 2169-2178, 2006. |
8 | S. Gao, I. W. H. Tsang, L. T. Chia, “Sparse representation with kernels”, Image Processing, 22(2): 423-434, 2013. |
9 | H. Zhang, A. Berg, M. Maire, et al., “SVM-KNN: Discriminative nearest neighbor classification for visual category recognition,” Computer Vision and Pattern Recognition, 2: 2126-2136, 2006. |
10 | M. L. Zhang, Z. H. Zhou, “ML-KNN: A lazy learning approach to multi-label learning,” Pattern recognition, 40(7): 2038-2048, 2007. DOI ScienceOn |
11 | H. Lee, A. Battle, R. Raina and A. Y. Ng, “Efficient sparse coding algorithms,” Advances in neural information processing systems, 801-808 2006. |
12 | L. Fei-Fei, R. Fergus, P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” Computer Vision and Image Understanding, 106(1): 59-70, 2007. DOI ScienceOn |
13 | L. Bourdev, S. Maji, J. Malik, “Describing people: A poselet-based approach to attribute classification,” Computer Vision (ICCV), pp. 1543-1550, 2011. |
14 | G. Griffin, A. Holub, and P. Perona, “Caltech-256 object category dataset. Technical Report USB/CSD-04-1366,” California Institute of Technology, 2007. |
15 | C. Vondrick, A. Khosla, T. Malisiewicz, et al., “Hoggles: Visualizing object detection features,” Computer Vision (ICCV), 1-8, 2013. |
16 | T. Malisiewicz, A. Gupta, A. A. Efros, “Ensemble of exemplar-svms for object detection and beyond,” Computer Vision (ICCV), 89-96, 2011. |
17 | D. G. Lowe, “Distinctive image features from scaleinvariant keypoints,” International journal of computer vision, 60(2): 91-110, 2004. DOI |
18 | A. Plinge, R Grzeszick, G. A. Fink, “A bag-of-features approach to acoustic event detection”, Acoustics, Speech and Signal Processing, 3704-3708, 2014. |
19 | J. C. Yang, K. Yu, Y. H. Gong and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” Computer Vision Pattern Recognition, 1794-1801, 2009. |
20 | J. Yu, M. Jeon, W. Pedrycz, “Weighted feature trajectories and concatenated bag-of-features for action recognition”, Neurocomputing, 131: 200-207, 2014. DOI ScienceOn |
21 | A. Bocsh, A. Zisserman, et al., “Image Classification using Random Forests and Ferns,” Computer Vision (ICCV), 1-8, 2007. |
22 | A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, “Weak hypotheses and boosting for generic object detection and recognition,” Computer Vision (ECCV), 71-84, 2004. |
23 | O. Boiman, E. Shechtman, M. Irani, “In defense of Nearest-Neighbor based image classification,” Computer Vision Pattern Recognition, 1-8, 2008. |
24 | D. Arthur, S. Vassilvitskii, “K-means++: The advantages of careful seeding,” Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, 1027-1035, 2007. |
25 | S. Agarwal, S. Yadav, K. Singh., “K-means versus K-means++ clustering technique,” Students Conference on Engineering and Systems, 1-6, 2012. |
26 | K. Wagstaff, C. Cardie, S. Rogers, et al., “Constrained K-means clustering with background knowledge,” International Conference on Machine Learning, 1: 577-584, 2001. |