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International Journal of Computer Vision

Erschienen in:

01.10.2012

Improving Image Classification Using Semantic Attributes

verfasst von: Yu Su, Frédéric Jurie

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2012

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Abstract

The Bag-of-Words (BoW) model—commonly used for image classification—has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.

Vorheriger Artikel Minimizing Energies with Hierarchical Costs

Nächster Artikel Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds

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Bosch, A., Zisserman, A., & Munoz, X. (2006). Scene classification via pLSA. In ECCV.

Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1–27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Chapelle, O., Haffner, P., & Vapnik, V. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5), 1055–1064. CrossRef

Csurka, G., Dance, C., Fan, L., Willamowski, J., & Bray, C. (2004). Visual categorization with bags of keypoints. In Proc. workshop on statistical learning in computer vision, at ECCV.

Delaitre, V., Laptev, I., & Sivic, J. (2010). Recognizing human actions in still images: a study of bag-of-features and part-based representations. In BMVC.

Deselaers, T., & Ferrari, V. (2011). Visual and semantic similarity in ImageNet. In CVPR.

Everingham, M., Van Gool, L., Williams, C., Winn, J., & Zisserman, A. (2007). The PASCAL visual object classes challenge 2007 results. http://www.pascal-network.org/challenges/VOC/voc2007/.

Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009). Describing objects by their attributes. In CVPR.

Fei-Fei, L., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In CVPR.

Gehler, P., & Nowozin, S. (2009). On feature combination for multiclass object classification. In ICCV.

van Gemert, J., Veenman, C., Smeulders, A., & Geusebroek, J. M. (2010). Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7), 1271–1283. CrossRef

Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset. Tech. rep. 7694. California Institute of Technology.

Harzallah, H., Jurie, F., & Schmid, C. (2009). Combining efficient object localization and image classification. In ICCV.

Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proc. of uncertainty in artificial intelligence.

Ji, R., Yao, H., Sun, X., Zhong, B., & Gao, W. (2010). Towards semantic embedding in visual vocabulary. In CVPR.

Khan, F., van de Weijer, J., & Vanrell, M. (2009). Top-down color attention for object recognition. In ICCV.

Kittler, J., Hatef, M., Duin, R., & Matas, J. (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239. CrossRef

Kumar, N., Berg, A., Belhumeur, P., & Nayar, S. (2009). Attribute and simile classifiers for face verification. In ICCV.

Lampert, C., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer. In CVPR.

Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In CVPR.

Leung, T., & Malik, J. (2001). Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43, 29–44. MATHCrossRef

Li, L., Su, H., Xing, E., & Fei-Fei, L. (2010a). Object bank: a high-level image representation for scene classification & semantic feature sparsification. In NIPS.

Li, L. J., Wang, C., Lim, Y., Blei, D., & Fei-Fei, L. (2010b). Building and using a semantivisual image hierarchy. In CVPR.

Liu, J., Yang, Y., & Shah, M. (2009). Learning semantic visual vocabularies using diffusion distance. In CVPR.

Moosmann, F., Triggs, B., & Jurie, F. (2007). Fast discriminative visual codebooks using randomized clustering forests. In NIPS.

Morioka, N., & Satoh, S. (2010). Building compact local pairwise codebook with joint feature space clustering. In ECCV.

Perronnin, F., Senchez, J., et al. (2010). Large-scale image categorization with explicit data embedding. In CVPR.

Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8(3), 382–439. CrossRef

Saghafi, B., Farahzadeh, E., Rajan, D., & Sluzek, A. (2010). Embedding visual words into concept space for action and scene recognition. In BMVC.

Sivic, J., & Zisserman, A. (2003). Video Google: a text retrieval approach to object matching in videos. In ICCV.

Sivic, J., Russell, B., Efros, A., Zisserman, A., & Freeman, W. (2005). Discovering objects and their location in images. In ICCV.

Sivic, J., Russell, B., Zisserman, A., Freeman, W., & Efros, A. (2008). Unsupervised discovery of visual object class hierarchies. In CVPR.

Su, Y., & Jurie, F. (2011). Visual word disambiguation by semantic contexts.

Su, Y., Allan, M., & Jurie, F. (2010). Improving object classification using semantic attributes. In BMVC.

Torresani, L., Szummer, M., & Fitzgibbon, A. (2010). Efficient object category recognition using classemes. In ECCV.

Ullah, M., Parizi, S., & Laptev, I. (2010). Improving bag-of-features action recognition with non-local cues. In BMVC.

Vogel, J., & Schiele, B. (2007). Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2), 133–157. CrossRef

Wang, G., Hoiem, D., & Forsyth, D. (2009). Learning image similarity from Flickr groups using stochastic intersection kernel machines. In ICCV.

Winn, J., Criminisi, A., & Minka, T. (2005). Object categorization by learned universal visual dictionary. In ICCV.

Xiao, J., Hays, J., Ehinger, K., Oliva, A., & Torralba, A. (2010). Sun database: large-scale scene recognition from abbey to zoo. In CVPR.

Yang, J., Li, Y., Tian, Y., Duan, L., & Gao, W. (2009). Group-sensitive multiple kernel learning for object categorization. In ICCV.

Yuan, J., Wu, Y., & Yang, M. (2007). Discovery of collocation patterns: from visual words to visual phrases. In CVPR.

Zhang, Y., & Chen, T. (2009). Efficient kernels for identifying unbounded-order spatial features. In CVPR.

Zheng, Y., Zhao, M., Neo, S., Chua, T., & Tian, Q. (2008). Visual synset: towards a higher-level visual representation. In CVPR.

Zhou, X., Yu, K., Zhang, T., & Huang, T. (2010). Image classification using super-vector coding of local image descriptors. In ECCV.

Titel: Improving Image Classification Using Semantic Attributes
verfasst von: Yu Su
Frédéric Jurie
Publikationsdatum: 01.10.2012
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2012
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-012-0529-4

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