Layered Self-Organizing Map for Image Classification in Unrestricted Domains
Abstract
The inherent difficultly in unrestricted image domain classification is due to the many different features exhibited by images. Efforts made toward classification of abstract features tend to focus on a single attribute. Without a method of unifying descriptors, it becomes very difficult to perform multi-feature analysis. Extending the concept of the Self-Organizing Feature Map to include multiple competitive layers, it has been possible to create a new type of Artificial Neural Network capable of analyzing image and signal datasets with multiple feature descriptors concurrently in a powerful yet computationally light manner. Compared to standard CBIR retrieval approach, a marked increase in the precision of clustering of 13 points has been achieved, along with a reduction in computation time.
Keywords
self-organizing map image classification features image retrievalReferences
- 1.Majunath, B.S., Salembier, P.H., Sikora, T.: Introduction to MPEG-7. Wiley (2002)Google Scholar
- 2.Kohonen, T.: Self-organizing map, 3rd edn. Springer (2000)Google Scholar
- 3.Kirk, J.S., Chang, D.-J.: Zurada. J.M.: A self-organizing map with dynamic architecture for efficient color quantization. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), vol. 3, pp. 2128–2132 (2001)Google Scholar
- 4.Arias, S.: Gomez. H.: Satellite Image Classification by Self-Organized Map on GRID Computing Infrastructures. In: Proceedings of the Second EELA-2 (2009)Google Scholar
- 5.Lu, S., Segall, R.S.: Multi-SOM: an Algorithm for High-Dimensional, Small Size Datasets. Journal of Systemics, Cybernetics and Informatics 11(2), 41–46 (2013)Google Scholar
- 6.Olteanu, M., Villa-Vialaneix, N., Cierco-Ayrolles, C.: Multiple Kernel Self-Organizing Maps. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2013)Google Scholar
- 7.Martín-Merino, M., Muñoz, A.: Extending the SOM Algorithm to Visualize Word Relationships. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 228–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 8.Breiteneder, C., Merkl, D., Eidenberger, H.: Merging Image Features by Self-organizing Maps in Coats of Arms Retrieval. In: Proceedings of European Conference on Electronic Imaging and the Visual Arts, Berlin, Germany (1999)Google Scholar
- 9.Rahman, M.: Image Search in a Visual Concept Feature Space with SOM-Based Clustering and Modified Inverted Indexing. In: Application and Novel algorithm Design, pp. 173–188. Intech. (2011)Google Scholar
- 10.Oja, E., Laaksonen, J., Koskela, M., Brandt, S.: Self-Organizing Maps for Content-Based Image Database Retrieval. In: Kohonen Maps, pp. 349–362. Elsevier (1999)Google Scholar
- 11.Huang, J., Ravi Kumar, S., Mitra, M., Zhu, W.-J., Zabih, R.: Image Indexing Using Color Correlograms. In: Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 762–768 (1997)Google Scholar
- 12.Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(6), 583–598 (1991)CrossRefGoogle Scholar
- 13.Naito, M., Hoashi, K., Matsumoto, K., Shishibori, M., Kita, K., Kutics, A., Nakagawa, A., Sugaya, F., Nakajima, Y.: High-Level Feature Extraction Experiments for TRECVID 2007. In: TRECVID 2007 (2007)Google Scholar
- 14.Kondo, I., Kutics, A., Tanaka, H., Sakano, H.: A new texture descriptor using steerable filters, In: Technical Report of IEICE, Vol. 104, No. 573 (PRMU2004), pp. 13-18 (2004)Google Scholar