Abstract
Most recent category-level object recognition systems work with visual words, i.e. vector quantized local descriptors. These visual vocabularies are usually constructed by using a single method such as K-means for clustering the descriptor vectors of patches sampled either densely or sparsely from a set of training images. Instead, in this paper we propose a novel methodology for building efficient codebooks for visual recognition using clustering aggregation techniques: the Visual Word Aggregation (VWA). Our aim is threefold: to increase the stability of the visual vocabulary construction process; to increase the image classification rate; and also to automatically determine the size of the visual codebook. Results on image classification are presented on the testbed PASCAL VOC Challenge 2007.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine Learning 56, 89–113 (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV (2004)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: ICML (2004)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Transactions on Knowledge Discovery from Data 1(1), 4 (2007)
Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: CVPR (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Leibe, B., Mikolajczyk, K., Schiele, B.: Efficient clustering and matching for object class recognition. In: BMVC (2006)
López-Sastre, R.J., Tuytelaars, T., Acevedo-Rodríguez, J., Maldonado-Bascón, S.: Towards a more discriminative and semantic visual vocabulary. Computer Vision and Image Understanding 115(3), 415–425 (2011)
Lowe, D.: Object recognition from local scale-invariant features. In: ICCV (1999)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10), 1615–1630 (2005)
Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS (2006)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR, pp. 2161–2168 (2006)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling scenes with local descriptors and latent aspects. In: ICCV (2005)
van de Sande, K., Gevers, T., Snoek, C.: Evaluation of color descriptors for object and scene recognition. In: CVPR (2008)
Tuytelaars, T.: Dense interest points. In: CVPR (2010)
Wang, H., Shan, H., Banerjee, A.: Bayesian cluster ensembles. In: SDM (2009)
Yuan, J., Wu, Y.: Context-aware clustering. In: CVPR (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
López-Sastre, R.J., Renes-Olalla, J., Gil-Jiménez, P., Maldonado-Bascón, S. (2011). Visual Word Aggregation. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_84
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)