Abstract
We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment multiple images into a consistent set of image regions. The segmentations are provided at several levels of granularity and links among them are automatically provided. Model training and inference in it is faster than most local feature extraction algorithms, and yet the provided image segmentation, and the segment matching among images provide a rich backdrop for image recognition, segmentation and registration tasks.
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Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR 2005 (2005)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: ICCV 2005 (2005)
Russell, B.C., Efros, A., Sivic, S., Freeman, W.T., Zisserman, A.: Segmenting Scenes by Matching Image Composites. In: NIPS 2009 (2009)
Leibe, B., et al.: An implicit shape model for combined object categorization and segmentation. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 508–524. Springer, Heidelberg (2006)
Ferrari, V., Zissermann, A.: Learning Visual Attributes. In: NIPS 2007 (2007)
Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR 2006 (2006)
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: ICCV 2005 (2005)
Jojic, N., Perina, A., Cristani, M., Murino, V., Frey, B.J.: Stel component analysis: Modeling spatial correlations in image class structure. In: CVPR 2009 (2009)
Jojic, N., Caspi, Y.: Capturing image structure with probabilistic index maps. In: CVPR 2004 (2004)
Russell, B., et al.: Using Multiple Segmentations to Discover Objects and their Extent in Image Collections. In: CVPR 2006 (2006)
Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1863–1868 (2006)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vision 38, 15–33 (2000)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR 2008 (2008)
Perina, A., et al.: A Hybrid Generative/discriminative Classification Framework Based on Free-energy Terms. In: ICCV 2009 (2009)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: ICCV 2005 (2005)
Cao, L., Fei-Fei, L.: Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes. In: ICCV 2007 (2007)
Winn, J., Jojic, N.: LOCUS: Learning Object Classes with Unsupervised Segmentation. In: ICCV 2005 (2005)
Jojic, N., Winn, J., Zitnick, L.: Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. In: CVPR 2006 (2006)
Boiman, O., Shechtman, E.: In Defense of Nearest-Neighbor Based Image Classification. In: CVPR 2008 (2008)
Yang, J., Yuz, K., Gongz, Y., Huang, T.: Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. In: CVPR 2009 (2009)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Active Learning with Gaussian Processes for Object Categorization. In: ICCV 2007 (2007)
Sun, M., Su, H., Savarese, S., Fei-Fei, L.: A multi-view probabilistic model for 3d object classes. In: CVPR 2009 (2009)
Stauffer, C., Miller, E., Tieu, K.: Transform-invariant image decomposition with similarity templates. In: NIPS 2002 (2002)
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Perina, A., Jojic, N., Castellani, U., Cristani, M., Murino, V. (2010). Object Recognition with Hierarchical Stel Models. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15567-3_2
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DOI: https://doi.org/10.1007/978-3-642-15567-3_2
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