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Metric space transformation optimizing content-based image retrieval and visual analysis evaluation

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Author(s):
Letrícia Pereira Soares Avalhais
Total Authors: 1
Document type: Master's Dissertation
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
Defense date:
Examining board members:
Agma Juci Machado Traina; Célia Aparecida Zorzo Barcellos; João do Espírito Santo Batista Neto
Advisor: Agma Juci Machado Traina
Abstract

The semantic gap problem has been a major focus of research in the development of content-based image retrieval (CBIR) systems. In this context, the most promising research focus primarily on the inference of continuous feature weights and feature selection. However, the traditional processes of continuous feature weighting are computationally expensive and feature selection is equivalent to a binary weighting. Aiming at alleviating the semantic gap problem, this master dissertation proposes two methods for the transformation of metric feature spaces based on the inference of transformation functions using Genetic Algorithms. The WF method infers weighting functions and the TF method infers transformation functions for the features. Compared to the existing methods, both proposed methods provide a drastic searching space reduction by limiting the search to the choice of an ordered set of transformation functions. Visual analysis of the transformed space and precision. vs. recall graphics confirm that both TF and WF outperform the traditional feature eighting methods. Additionally, we found that TF method significantly outperforms WF regarding the query similarity accuracy by performing non linear feature space transformation, as found in the visual analysis. (AU)

FAPESP's process: 09/04232-8 - Visual Data Analysis to Improve Metric Access Methods
Grantee:Letrícia Pereira Soares Avalhais
Support type: Scholarships in Brazil - Master