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Exploring Extended Multi-Scale Dissimilarity Spaces for Prototype Selection

This coffee talk presents an in-depth exploration of extended multi-scale dissimilarity spaces, focusing on prototype selection methods. Key techniques include Separate Forward Selection, Genetic Algorithms, and Random Selection across various datasets, including texture and object classification problems. We discuss the computational complexity associated with these approaches and their empirical performance using datasets comprised of diverse object types like chickens and specific textures. Join us to learn how these methods enhance prototype selection effectiveness in multi-scale analysis.

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Exploring Extended Multi-Scale Dissimilarity Spaces for Prototype Selection

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  1. Prototype Selection for extended multi-scale dissimilarity spaces Coffee talk, Bob Duin, 7 Dec 2012 Sept 2012, Yenisel Plasencia, Yan Li, Bob Duin, Mauricio Orozco Alzate, Marco Loog, Edel García-Reyes Coffee Talk

  2. Multi-scale extended dissimilarity spaces 32 x 32 16 x 16 8 x 8 DS1 = [ D1 D2 D3 ] Separate DS2 = [ D1 D2 D3 ] Aligned DS3 = D1 + D2 + D3 Averaged

  3. Prototype selection Separate Forward Selection Separate Genetic Algorithm Separate Random Selection Aligned Forward Selection Aligned Random Selection Averaged Forward Selection Averaged Random Selection Coffee Talk

  4. Colon dataset Ext Separate FS Ext Separate GA Ext Separate Rand Ext Aligned FS Ext Aligned Rand Avg FS Avg Rand 200 objects 1000 objects

  5. Texture dataset Ext Separate FS Ext Separate GA Ext Separate Rand Ext Aligned FS Ext Aligned Rand Avg FS Avg Rand 222 objects 666 objects

  6. Chicken Pieces Ext Separate FS Ext Separate GA Ext Separate Rand Ext Aligned FS Ext Aligned Rand Avg FS Avg Rand 170 objects 350 objects

  7. Computational Complexity Textures

  8. Coffee Talk

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