Abstract
To support large-scale visual recognition, it is critical to train a large number of classifiers with high discrimination power. To achieve this task, in this paper a hierarchical visual tree is constructed for organizing a large number of object classes and image concepts according to their inter-concept visual correlations. Based on the hierarchical visual tree, a novel approach is proposed for learning multi-scale group-based dictionary to support discriminative bag-of-visual-words (BoW-based) image representation. In addition, a structural learning approach is developed to enable large-scale classifier training over such hierarchical visual tree. We have also compared the performance of our hierarchical visual tree with traditional label tree over large-scale image collections.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barnard, K., Forsyth, D.: Learning the semantics of words and pictures. In: ICCV, vol. 2, pp. 408–415 (2001)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Cilibrasi, R., Vitanyi, P.: The google similarity distance. IEEE Trans. on Knowledge and Data Engineering 19(3), 370–383 (2007)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, vol. 1, p. 22 (2004)
Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (June 2009)
Dong, P., Mei, K., Zheng, N., Lei, H., Fan, J.: Training inter-related classifiers for automatic image classification and annotation. Pattern Recognition 46(5), 1382–1395 (2013)
Fan, J., Gao, Y., Luo, H.: Integrating concept ontology and multitask learning to achieve more effective classifier training for multilevel image annotation. IEEE Trans. on Image Processing 17(3), 407–426 (2008)
Fan, J., Gao, Y., Luo, H., Jain, R.: Mining multilevel image semantics via hierarchical classification. IEEE Trans. on Multimedia 10(2), 167–187 (2008)
Fan, J., Luo, H., Hacid, M.S.: Mining images on semantics via statistical learning. In: ACM SIGKDD, pp. 22–31 (2005)
Fan, J., Shen, Y., Yang, C., Zhou, N.: Structured max-margin learning for inter-related classifier training and multilabel image annotation. IEEE Trans. on Image Processing 20(3), 837–854 (2011)
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, vol. 2, pp. 524–531 (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, vol. 2, pp. 2169–2178 (2006)
Mairal, J., Ponce, J., Sapiro, G., Zisserman, A., Bach, F.R.: Supervised dictionary learning. In: NIPS, pp. 1033–1040 (2008)
Marszalek, M., Schmid, C.: Semantic hierarchies for visual object recognition. In: CVPR, pp. 1–7 (2007)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Moosmann, F., Triggs, B., Jurie, F.: Fast Discriminative Visual Codebooks using Randomized Clustering Forests. In: NIPS, pp. 985–992 (2007)
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR, vol. 2, pp. 2161–2168 (2006)
Patwardhan, S.: Incorporating dictionary and corpus information into a context vector measure of semantic relatedness. Master thesis, University of Minnesota, Duluth (2003)
Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: similarity - measuring the relatedness of concepts. In: AAAI, pp. 1024–1025 (2004)
Ries, C., Romberg, S., Lienhart, R.: Towards universal visual vocabularies. In: ICME, pp. 1067–1072 (July 2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on PAMI 22(8), 888–905 (2000)
Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. on PAMI 22(12), 1349–1380 (2000)
Vasconcelos, N.: Image indexing with mixture hierarchies. In: CVPR, vol. 1, p. I (2001)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org
Wang, Y., Gong, S.: Refining image annotation using contextual relations between words. In: CIVR, pp. 425–432 (2007)
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: ICCV, vol. 2, pp. 1800–1807 (October 2005)
Yang, L., Jin, R., Sukthankar, R., Jurie, F.: Unifying discriminative visual codebook generation with classifier training for object category recognition. In: CVPR, pp. 1–8 (2008)
Zhang, J., MarszaLek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73, 213–238 (2007)
Zhang, Q., Li, B.: Discriminative k-svd for dictionary learning in face recognition. In: CVPR, pp. 2691–2698 (June 2010)
Zhou, N., Shen, Y., Peng, J., Fan, J.: Learning inter-related visual dictionary for object recognition. In: CVPR, pp. 3490–3497 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Lei, H., Zhou, N., Mei, K., Dong, P., Fan, J. (2013). Constructing Hierarchical Visual Tree for Discriminative Image Representation and Classification. 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_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-03731-8_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
eBook Packages: Computer ScienceComputer Science (R0)