Abstract
In this paper we propose a new and effective scheme for applying frequent itemset mining to image classification tasks. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During the construction of the FLHs, we pay special attention to keep all the local histogram information during the mining process and to select the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and some proposed extensions to exploit other visual cues such as colour or global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the art results on various image classification benchmarks.
Chapter PDF
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)
Uno, T., Asai, T., Uchida, Y., Arimura, H.: Lcm: An efficient algorithm for enumerating frequent closed item sets. In: FIMI (2003)
Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective classification. In: ICDE, pp. 716–725 (2007)
Nowozin, S., Tsuda, K., Uno, T., Kudo, T., Bakir, G.: Weighted substructure mining for image analysis. In: CVPR (2007)
Yao, B., Fei-Fei, L.: Grouplet: A structured image representation for recognizing human and object interactions. In: CVPR (2010)
Quack, T., Ferrari, V., Leibe, B., Van Gool, L.: Efficient mining of frequent and distinctive feature configurations. In: ICCV (2007)
Yuan, J., Wu, Y., Yang, M.: Discovery of collocation patterns: from visual words to visual phrases. In: CVPR (2007)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Work. on Statistical Learning in CV, pp. 1–22 (2004)
Agarwal, A., Triggs, B.: Multilevel image coding with hyperfeatures. Int. J. Comput. Vision 78, 15–27 (2008)
Sivic, J., Zisserman, A.: Video data mining using configurations of viewpoint invariant regions. In: CVPR (2004)
Yuan, J., Luo, J., Wu, Y.: Mining compositional features for boosting. In: CVPR (2008)
Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: ICDE, pp. 169–178 (2008)
Gilbert, A., Illingworth, J., Bowden, R.: Fast realistic multi-action recognition using mined dense spatio-temporal features. In: ICCV, pp. 925–931 (2009)
Yuan, J., Yang, M., Wu, Y.: Mining discriminative co-occurrence patterns for visual recognition. In: CVPR, pp. 2777–2784 (2011)
Kim, S., Jin, X., Han, J.: Disiclass: discriminative frequent pattern-based image classification. In: Tenth Int. Workshop on Multimedia Data Mining (2010)
Quack, T., Ferrari, V., Van Gool, L.: Video Mining with Frequent Itemset Configurations. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 360–369. Springer, Heidelberg (2006)
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR, pp. 1778–1785 (2009)
Yun, U., Leggett, J.J.: Wfim: Weighted frequent itemset mining with a weight range and a minimum weight. In: SDM 2005 (2005)
Yang, Y., Newsam, S.: Spatial pyramid co-occurrence for image classification. In: ICCV (2011)
Yimeng Zhang, T.C.: Efficient kernels for identifying unbounded-order spatial features. In: CVPR (2009)
Boureau, Y.L., Bach, F., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: CVPR (2010)
Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing itemset patterns: a profile-based approach. In: ACM SIGKDD (2005)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)
Svetlana Lazebnik, C.S., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Opelt, A., Fussenegger, M., Pinz, A., Auer, P.: Weak Hypotheses and Boosting for Generic Object Detection and Recognition. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part II. LNCS, vol. 3022, pp. 71–84. Springer, Heidelberg (2004)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: ICVGIP, pp. 722–729 (2008)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, pp. 1150–1157 (1999)
van de Weijer, J., Schmid, C.: Applying color names to image description. In: ICIP, pp. 493–496 (2007)
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR (2008)
Shahbaz Khan, F., van de Weijer, J., Vanrell, M.: Top-down color attention for object recognition. In: ICCV, pp. 979–986 (2009)
Xie, N., Ling, H., Hu, W., Zhang, X.: Use bin-ratio information for category and scene classification. In: CVPR, pp. 2313–2319 (2010)
Tuytelaars, T., Fritz, M., Saenko, K., Darrell, T.: The nbnn kernel. In: ICCV, pp. 1824–1831 (2011)
Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: BMVC (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernando, B., Fromont, E., Tuytelaars, T. (2012). Effective Use of Frequent Itemset Mining for Image Classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33718-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-33718-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33717-8
Online ISBN: 978-3-642-33718-5
eBook Packages: Computer ScienceComputer Science (R0)