| Oja, E., Laaksonen, J., Koskela, M., and Brandt, S. (1999). Self-organizing maps for content-based image database retrieval. In Oja, E. and Kaski, S., editors, Kohonen Maps, pages 349--362. Elsevier, Amsterdam. |
....In reporting results we report the number of codebook vector parameters the number of the rest of the parameters is substantially smaller and constant in all experiments. Data and variables. As a data set we used features from 1000 images that have been used for content based image retrieval in [6] 3 . Each image is represented by a vector of 144 features (variables) The features fall naturally into three categories related to their origin: FFT, RGB, and texture. It is reasonable to assume that dependences between variables in di erent categories are weak 4 . Thus, the data provides an ....
....contained variables from one variable category only. Furthermore, the improved model cost shows that presented IVGAVQ algorithm performs well in the IVGA task. 3 We are grateful to the PicSOM project members for kindly providing us with the image data set. 4 This assumption has been made in [6] prior to modeling each variable category with a di erent SOM. Table 1. Results of the grouping when IVGAVQ was run for the combined data set A B C. Negative costs are due to leaving out the constant factor in Eq. 1. Group Variables Belong to Cost Codebook Parameters data set vectors 1 1,6 ....
E. Oja, J. Laaksonen, M. Koskela, and S. Brandt. Self-organizing maps for contentbased image database retrieval. In E. Oja and S. Kaski, eds., Kohonen Maps, pp. 349-362. Elsevier, 1999.
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Oja, E., Laaksonen, J., Koskela, M., and Brandt, S. (1999). Self-organizing maps for content-based image database retrieval. In Oja, E. and Kaski, S., editors, Kohonen Maps, pages 349--362. Elsevier, Amsterdam.
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