A CONTRIBUTION TO SEMANTIC DESCRIPTION OF IMAGES AND VIDEOS: AN APPLICATION OF SOFT BIOMETRICS DOCTORAL THESIS

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FEDERAL UNIVERSITY OF TECHNOLOGY - PARAN ´ A GRADUATE PROGRAM IN ELECTRICAL AND COMPUTER

FEDERAL UNIVERSITY OF TECHNOLOGY - PARAN ´ A GRADUATE PROGRAM IN ELECTRICAL AND COMPUTERENGINEERING HUGO ALBERTO PERLIN A CONTRIBUTION TO SEMANTIC DESCRIPTION OF IMAGES

AND VIDEOS: AN APPLICATION OF SOFT BIOMETRICS DOCTORAL THESIS

AND VIDEOS: AN APPLICATION OF SOFT BIOMETRICS

  CURITIBA2015 Doctoral Thesis presented to the Graduate Program in Electrical and Computer Engineering of theFederal University of Technology - Paran´a as partial fulfillment of the requirements for the title of“Doctor of Science (D.Sc.)” – Concentration Area: Computer Engineering. Universidade Tecnológica Federal do Paraná - Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial.

Título da Tese Nº. 129 A Contribution to Semantic Description of Images and Videos: an Application of Soft Biometrics

Hugo Alberto Perlin Orientador: Prof. Dr. Heitor Silvério Lopes Coorientador: Prof. Dr

  1 sigm (z) = (1) z 1 z−ze − e tanh (z) = (2) z ze + e w 1bias w1 x1 w2 x2 nyz=∑ (x i .w i ) i ϕ(.) =0w n x n Connection Weitghts Weighted Summation Output InputActivation Function Figure 3: Example of a neuron, showing the inputs, the weighted sum, the activation function and the output.i i Given a dataset X in the form of (x , y ), where x is the input data and y is the label. The selection mechanism compares the quality of u against x , using a i i simple greedy criterion, as shown in Equation(8). (G+1) (G+1) (G) ~u if ( f (~u ) < f (~x ))) (G+1) i i i (8)~x = i  (G) otherwise~x i The application of the two operators, mutation and crossover, followed by the selection procedure, is probabilistically done for all elements of the population, and the whole procedureis repeated for several iterations until a stopping criterion is met.

Formally, feature extraction is a mapping function from R → R of the original data vector and d is the length of the transformed vector. It is important to notice

  Create DE population Evaluate the fitness of each individual Generate mutant vectors Generate trial vectors trought the extractor with the feature matrix Forward the images Execute K-means Fitness Evaluation Procedure Evaluate the fitness of each trial vector using crossover With the assigment vector calculate SC fitness of the individual Assignt SC as the between trial and original vectors Execute the selection Stopping CriterionReached? xi − X maxminmaxNorm (14)(x i ) = X max − X min where, X is the maximum component of the vector, X is the minimum component maxmin of the vector and x is a single component.i Next, the predictive power of each feature regarding the class is accessed by applying some well-known statistical methods: Information Gain ( IG), Spearman’s rank correlationcoefficient R, and the Kruskal-Wallis score ( KW).

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