Advertisement

Inter-Cluster Features for Medical Image Classification

  • Siyamalan Manivannan
  • Ruixuan Wang
  • Emanuele Trucco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Feature encoding plays an important role for medical image classification. Intra-cluster features such as bag of visual words have been widely used for feature encoding, which are based on the statistical information within each clusters of local features and therefore fail to capture the inter-cluster statistics, such as how the visual words co-occur in images. This paper proposes a new method to choose a subset of cluster pairs based on the idea of Latent Semantic Analysis (LSA) and proposes a new inter-cluster statistics which capture richer information than the traditional co-occurrence information. Since the cluster pairs are selected based on image patches rather than the whole images, the final representation also captures the local structures present in images. Experiments on medical datasets show that explicitly encoding inter-cluster statistics in addition to intra-cluster statistics significantly improves the classification performance, and adding the rich inter-cluster statistics performs better than the frequency based inter-cluster statistics.

Keywords

Visual Word Word Pair Image Patch Latent Semantic Analysis Cluster Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Manivannan, S., Wang, R., Trucco, E., Hood, A.: Automatic normal-abnormal video frame classification for colonoscopy. In: ISBI (2013)Google Scholar
  2. 2.
    Yang, Y., Newsam, S.: Spatial pyramid co-occurrence for image classification. In: ICCV, pp. 1465–1472 (2011)Google Scholar
  3. 3.
    Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: AGIS, pp. 270–279 (2010)Google Scholar
  4. 4.
    Lowe, D.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  5. 5.
    Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: CVPR, pp. 3304–3311 (2010)Google Scholar
  6. 6.
    Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: CVPR, pp. 1–8 (2007)Google Scholar
  7. 7.
    Chen, T., Yap, K.H., Chau, L.P.: From universal bag-of-words to adaptive bag-of-phrases for mobile scene recognition. In: ICIP, pp. 825–828 (2011)Google Scholar
  8. 8.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)CrossRefGoogle Scholar
  9. 9.
    Zelikovitz, S., Hirsh, H.: Using LSI for text classification in the presence of background text. In: ICIKM, pp. 113–118 (2001)Google Scholar
  10. 10.
    Kontostathis, A., Pottenger, W.M.: A framework for understanding latent semantic indexing performance. In: IPM, pp. 56–73 (2006)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. JMLR (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Siyamalan Manivannan
    • 1
  • Ruixuan Wang
    • 1
  • Emanuele Trucco
    • 1
  1. 1.CVIP, School of ComputingUniversity of DundeeUK

Personalised recommendations