A hybrid codebook model for object categorization using two-way clustering based codebook generation method

References

• O'Hara S, Draper B. Introduction to the bag of features paradigm for image classification and retrieval. arXiv preprint arXiv:1101.3354. 2011.
   Google Scholar
• Joachims T. Text categorization with support vector machines: Learning with many relevant features. Proceedings of the European conference on machine learning(ECML'98); Springer, Berlin, Heidelberg; 1998. p. 137–142.
   Google Scholar
• Baeza-Yates RA, Ribeiro-Neto B. Modern information retrieval. USA: Addison-Wesley Longman Publishing Co., Inc; 1999.
   Google Scholar
• Leung T, Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int J Comput Vis. 2001;43(1):29–44. doi: https://doi.org/10.1023/A:1011126920638
   Web of Science ®Google Scholar
• Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos. Ninth IEEE International Conference on Computer Vision (ICCV'03). 2003. p. 1470–1477. Nice. France.
   Google Scholar
• Csurka G, Dance C, Fan L, et al. Visual categorization with bags of keypoints. Workshop on statistical learning in computer vision (ECCV'04). 2004. p. 1–22.
   Google Scholar
• Lowe D. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60:91–110. doi: https://doi.org/10.1023/B:VISI.0000029664.99615.94
   Web of Science ®Google Scholar
• Avila S, Thome N, Cord M. BOSSA: Extended bow formalism for image classification. 18th IEEE International Conference on Image Processing (ICIP). 2011. p. 2909–2912. Brussels. Belgium.
   Google Scholar
• Boureau Y, Bach F, LeCun Y, et al. Learning mid-level features for recognition. The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010; San Francisco, CA; 13–18 June 2010; 2010. p. 2559–2566.
   Google Scholar
• Goh H, Thome N, Cord M, et al. Learning deep hierarchical visual feature coding. IEEE Trans Neural Netw Learn Syst. 2014;25:2212–2225. doi: https://doi.org/10.1109/TNNLS.2014.2307532
   PubMed Web of Science ®Google Scholar
• Wang L, Liu L, Zhou L, et al. Application of SVMs to the bag-of-features model: a kernel perspective. In: Ma Y, Guo G, editors. Support vector machines applications. Germany: Springer; 2014. p. 155–189.
   Google Scholar
• Lazebnik S, Cordelia Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2006); 17–22 June 2006; New York, NY; 2006. p. 2169–2178.
   Google Scholar
• Zhang E, Mayo M. Improving Bag-of-Words model with spatial information. 25th International Conference of Image and Vision Computing. 2010. p. 1–8. New Zealand..
   Google Scholar
• VanGemert J, Geusebroek J, Veenman C. Kernel codebooks for scene categorization. 10th European Conference on Computer Vision (ECCV'2008), 2008. PART III. p. 696–709. Marseille. France.
   Google Scholar
• Yang J, Yu K, Gong Y, et al. Linear spatial pyramid matching using sparse coding for image classification. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR'09); Miami, FL; 2009. p. 1794–1801.
   Google Scholar
• Zhang W, Surve A, Fern X, et al. Learning non-redundant codebooks for classifying complex objects. Proceedings of the 26th Annual International Conference on Machine Learning (ICML'09); ACM, Montreal, Quebec, Canada; 2009. p. 1241–1248.
   Google Scholar
• Jin Park D, Kim C. A hybrid bags-of-feature model for sports scene classification. J Sign Process Syst. 2015;81:249–263. doi: https://doi.org/10.1007/s11265-014-0946-4
   Web of Science ®Google Scholar
• Charrad M, BenAhmed M. Simultaneous clustering: a survey. Proceedings of 4th International Conference on Pattern Recognition and Machine Intelligence(PReMI'11); Springer, Moscow, Russia; 2011. p. 370–375.
   Google Scholar
• Cheng Y, Church GM. Biclustering of expression data. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMBOO). AAAI Press; 2000. p. 93–103. California.
   Google Scholar
• Dhillon IS. Co-clustering documents and words using bipartite spectral graph partitioning. Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press; 2001. p. 269–274. San Francisco. California.
   Google Scholar
• Hartigan JA. Direct clustering of a data matrix. J Am Stat Assoc. 1972;67(337):123–129. doi: https://doi.org/10.1080/01621459.1972.10481214
   Web of Science ®Google Scholar
• Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform. 2004;1:24–45. doi: https://doi.org/10.1109/TCBB.2004.2
   PubMed Web of Science ®Google Scholar
• Charrad M, Lechevallier Y, Ahmed MB. Block clustering for web pages categorization. Proceeding of 10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL' 09). 2009. p. 260–267. Burgos, Spain.
   Google Scholar
• Bichot CE. Co-clustering documents and words by minimizing the normalized cut objective function. J Math Model Algorithm. 2010;9(2):131–147. doi: https://doi.org/10.1007/s10852-010-9126-0
   Google Scholar
• Zhang Z, Xu Y, Yang XJ, et al. A survey of sparse representation: algorithms and applications. IEEE Access. 2015;3:490–530. doi: https://doi.org/10.1109/ACCESS.2015.2430359
   Web of Science ®Google Scholar
• Nowak E, Jurie F, Triggs B. Sampling strategies for bag-of-features image classification. Proceedings of Ninth European Conference of Computer Vision ECCV 2006, Springer; 2006. p. 490–503. Graz. Austria.
   Google Scholar
• Zhang J, Marszalek M, Lazebnik S, et al. Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis IEEE. 2007;73:213–238. doi: https://doi.org/10.1007/s11263-006-9794-4
   Web of Science ®Google Scholar
• Fei-Fei L, Perona P. A bayesian hierarchical model for learning natural scene categories. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 2005. p. 524–531. San Diego. CA. USA.
   Google Scholar
• Perronnin F, Dance C, Csurka G. Adapted vocabularies for generic visual categorization. Proceeding of 9th European Conference of Computer Vision (ECCV' 06). 2006. p. 464–475. Graz Austria..
   Google Scholar
• Quelhas P, Monay F, Odobez J-M, et al. IEEE Trans Pattern Anal Mach Intell. 2007;29(9):1575–1589. doi: https://doi.org/10.1109/TPAMI.2007.1155
   PubMed Web of Science ®Google Scholar
• Sudderth EB, Torralba A, Freeman WT, et al. Describing visual scenes using transformed objects and parts. Int J Comput Vis. 2008;77(1–3):291–330. doi: https://doi.org/10.1007/s11263-007-0069-5
   Web of Science ®Google Scholar
• Winn J, Criminisi A, Minka T. Object categorization by learned universal visual dictionary. Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV'05). 2005. p. 1800–1807. Beijing. China.
   Google Scholar
• Jurie F, Triggs B. Creating efficient codebooks for visual recognition. Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV'05). 2005. p. 604–610. Beijing. China.
   Google Scholar
• Dorko G, Schmid C. Object class recognition using discriminative local features. Technical report, IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005.
   Google Scholar
• Perronnin F. Universal and adapted vocabularies for generic visual categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2008; 30(7):1243–1256.
   Google Scholar
• Leibe B, Schiele B. Interleaved object categorization and segmentation. Proceedings of British Machine Vision Conference (BMVC'03); BMVA Press, Norwich, UK; 2003. p. 759–768.
   Google Scholar
• Agarwal S, Awan A, Roth D. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004; 26(11):1475–1490.
   Google Scholar
• Chang L, Duarte M, Enrique Sucar L, et al. A Bayesian approach for object classification based on clusters of SIFT local features. J Expert Syst Appl. 2012;39:1679–1686. doi: https://doi.org/10.1016/j.eswa.2011.06.059
   Web of Science ®Google Scholar
• Mikolajczyk K, Leibe B, Schiele B. Multiple object class detection with a generative model. IEEE Conference on Computer Vision and Pattern Recognition. 2006. p. 26–36. New York. USA.
   Google Scholar
• Nister D, Stewenius H. Scalable recognition with a vocabulary tree. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR'06). 2006. p. 2161–2168. New York. USA.
   Google Scholar
• Liu J, Shah M. Scene modeling using co-clustering. IEEE 11th International Conference on Computer Vision. 2007. p. 1–7. Rio de Janeiro. Brazil.
   Google Scholar
• Larlus D, Jurie F. Latent mixture vocabularies for object categorization. The 17th British Machine Vision Conference (BVMC '06), Sep 2006. pp.959–968. Edinburgh. United Kingdom.
   Google Scholar
• Moosmann F, Triggs B, Jurie F. 2007. Fast discriminative visual codebooks using randomized clustering forests. Proceeding of the Neural Information Processing Systems Conference (NIPS'07), Canada. p. 985–992.
   Google Scholar
• Wang L. Toward a discriminative codebook: codeword selection across multi-resolution. Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR'07). 2007. p. 1–8. Minneapolis. MN. USA.
   Google Scholar
• Lopez-Sastre RJ, Renes-Olalla J, Gil-Jimenez P, et al. Heterogeneous visual codebook integration via consensus clustering for visual categorization. IEEE Trans Circ Syst Vid Technol. 2013;23:1358–1368. doi: https://doi.org/10.1109/TCSVT.2013.2243058
   Web of Science ®Google Scholar
• Altintakan UL, Yazici A. Towards effective image classification using class-specific codebooks and distinctive local features. IEEE Trans Multimedia. 2015;17(3):323–332. doi: https://doi.org/10.1109/TMM.2014.2388312
   Web of Science ®Google Scholar
• Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process. Nov 2006;54(11):4311–4322. doi: https://doi.org/10.1109/TSP.2006.881199
   Web of Science ®Google Scholar
• Wang J, Yang J, Yu K. Locality-constrained linear coding for image classification. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'10). 2010. p. 3360–3367. San Francisco, CA, USA.
   Google Scholar
• Zhang Q, Li B. Discriminative K-SVD for dictionary learning in face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010. p. 2691–2698. San Francisco, CA, USA.
   Google Scholar
• Jiang Z, Lin Z, Davis L. Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell. Nov 2013;35(11):2651–2664. doi: https://doi.org/10.1109/TPAMI.2013.88
   PubMed Web of Science ®Google Scholar
• Yang M, Zhang L, Feng X. Fisher discrimination dictionary learning for sparse representation. Proceedings of the 2011 International Conference on Computer Vision. 2011. p. 543–550. Barcelona. Spain.
   Google Scholar
• Thiagarajan J, Spanias A. Learning dictionaries for local sparse coding in image classification. Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR). Nov 2011. p. 2014–2018. Pacific Grove, CA, USA.
   Google Scholar
• Quan Y, Xu Y, Sun Y. Sparse coding for classification via discrimination ensemble. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2016. p. 5839–5847. Las Vegas, NV, USA.
   Google Scholar
• Wang X, Lid Y, You S. Unidirectional representation based efficient dictionary learning. IEEE Trans Circuits Syst Video Technol. 2018;30(1):59–74. doi: https://doi.org/10.1109/TCSVT.2018.2886600
   Web of Science ®Google Scholar
• Farquhar J, Szedmak S, Meng H, et al. Improving bag-of-keypoints image categorisation: generative models and pdf-kernels. University of Southampton, UK; 2005. (Technical report, LAVA report).
   Google Scholar
• Liu L, Wang L, Liu X. In defense of soft-assignment coding. Proceedings of IEEE International Conference on Computer Vision (ICCV'11). Barcelona, Spain. 2011. p. 2486–2493.
   Google Scholar
• Ramanan A, Niranjan M. A review of codebook models in patch-based visual object recognition. J Signal Process Syst. 2012;68:333–352. Elsevier, North-Holland. doi: https://doi.org/10.1007/s11265-011-0622-x
   Web of Science ®Google Scholar
• Gao S, Tsang IW, Chia L. Sparse representation with kernels. IEEE Trans Image Process. 2013;22(2):423–434. doi: https://doi.org/10.1109/TIP.2012.2215620
   PubMed Web of Science ®Google Scholar
• Wang S, Wang Y. A multi-scale learning framework for visual categorization. 10th Asian Conference on Computer Vision (ACCV 2010). Part I. New Zealand. 2010. p. 310–322.
   Google Scholar
• Oliveira GL, Nascimento E, Vieira A. Sparse spatial coding: a novel approach for efficient and accurate object recognition. 2012 IEEE International Conference on Robotics and Automation. USA. May 2012. p. 2592–2598.
   Google Scholar
• Zhang C, Wang S, Huang Q, et al. Image classification using spatial pyramid robust sparse coding. J Pattern Recognit Lett. 2013;34:1046–1052. doi: https://doi.org/10.1016/j.patrec.2013.02.013
   Web of Science ®Google Scholar
• Lee H, Grosse R, Ranganath R. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning (ICML' 09). Montreal, Quebec, Canada. 2009. p. 609–616.
   Google Scholar
• Wang X, Wang L, Qiao Y. A comparative study of encoding, pooling and normalization methods for action recognition. Proceedings of 11th Asian Conference on Computer Vision (ACCV'12); Springer, Daejeon, Korea; 2012. p. 572–585.
   Google Scholar
• Barla A, Odone F, Verri A. Histogram intersection kernel for image classification. Proceedings of IEEE International Conference on Image Processing (ICIP'03), Barcelona, Spain; 2003. p. III–513–16.
   Google Scholar
• Fei-Fei L, Fergus R, Perona P. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Proceedings of IEEE Conference on CVPR Workshop of Generative Model Based Vision (WGMBV). Washington, DC, USA. 2004. p. 59–70.
   Google Scholar
• Wang C, Blei D, Fei-Fei L. Simultaneous image classification and annotation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09). Miami, FL, USA. 2009. p.1903–1910.
   Google Scholar
• VanGemert J, Snoek C, Veenman C, et al. Comparing compact codebooks for visual categorization. J Comput Vis Image Und. 2010;114:450–462. doi: https://doi.org/10.1016/j.cviu.2009.08.004
   Web of Science ®Google Scholar
• Marszalek M, Schmid C, Harzallah H, et al. Learning object representations for visual object class recognition. Visual Recognition Challange workshop, in conjunction with ICCV; 2007.
   Google Scholar
• vanGemert J, Veenman C, Smeulders A, et al. Visual word ambiguity. IEEE Trans Pattern Anal Mach Intell. 2009;32:1271–1283. doi: https://doi.org/10.1109/TPAMI.2009.132
   Web of Science ®Google Scholar
• Berg A, Berg T, Malik J. Shape matching and object recognition using low distortion correspondences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego, CA, USA. 2005. p. 26–33.
   Google Scholar
• Zhang H, Berg AC, Maire M. SVM-KNN: discriminative nearest neighbor classification for visual category recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA. 2006. p. 2126–2136.
   Google Scholar
• Griffin G, Holub A, Perona P. Caltech-256 object category dataset. California Institute of Technology; 2007. (Technical report).
   Google Scholar
• Boiman O, Shechtman E, Irani M. In defense of nearest-neighbor based image classification. 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). Anchorage, AK, USA. 2008. p. 1–8.
   Google Scholar
• Jain P, Kulis B, Grauman K. Fast image search for learned metrics. IEEE Conference on Computer Vision and Pattern Recognition(CVPR'08). Anchorage, AK, USA. 2008. p. 1–8.
   Google Scholar