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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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Independent Variable Group Analysis - Lagus, Alhoniemi, Valpola (2001)   (1 citation)  (Correct)

....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.


Bibliography of Self-Organizing Map (SOM) Papers.. - Merja Oja, Samuel.. (2002)   Self-citation (Oja)   (Correct)

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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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