mk:cec08

Summary

Auxiliary Objectives for the Evolutionary Multi-Objective Principal Color Extraction from Logo Images. Mario Köppen, Yutaka Kinoshita and Kaori Yoshida. In 2008 IEEE Congress on Evolutionary Computation (CEC08?), Proceedings, pages 3536-3542, 2008.

Abstract

In this paper, we present an approach to the selection of principal colors for the class of logo images. The approach is using multiple objectives that can be assigned to a color set, qualifying the selected colors as being principal colors of the image. Since all these objectives have a different preference, and have different computational complexity and granularity, it is not useful to put them all together into a single objective vector. Instead, a three stages procedure is proposed. The first stage optimizes only objectives of high relevance, and lower computational effort. Here, evolutionary multi-objective optimization is used. The second stage re-evaluates the Pareto set of the first stage according to an additional set of objectives. Finally, one solution of the Pareto set from the second stage is selected according to a single objective of highest preference. As suitable objectives for the first stage, the average minimum distance of the color set to the image pixels, together with the average number of pixel that are closer than a threshold have been found. The approach was studied on a number of logo images, and it could reconstruct the logo images of good visual quality from the found principal colors in the majority of the cases. The experiments also show that the result is usually improved by searching for more principal colors than are present in the logo image, and by repeating the process to find also small, but notable detail structures.

Bibtex entry

@INPROCEEDINGS { mk:cec08,
    ABSTRACT = { In this paper, we present an approach to the selection of principal colors for the class of logo images. The approach is using multiple objectives that can be assigned to a color set, qualifying the selected colors as being principal colors of the image. Since all these objectives have a different preference, and have different computational complexity and granularity, it is not useful to put them all together into a single objective vector. Instead, a three stages procedure is proposed. The first stage optimizes only objectives of high relevance, and lower computational effort. Here, evolutionary multi-objective optimization is used. The second stage re-evaluates the Pareto set of the first stage according to an additional set of objectives. Finally, one solution of the Pareto set from the second stage is selected according to a single objective of highest preference. As suitable objectives for the first stage, the average minimum distance of the color set to the image pixels, together with the average number of pixel that are closer than a threshold have been found. The approach was studied on a number of logo images, and it could reconstruct the logo images of good visual quality from the found principal colors in the majority of the cases. The experiments also show that the result is usually improved by searching for more principal colors than are present in the logo image, and by repeating the process to find also small, but notable detail structures. },
    AUTHOR = { Mario Köppen and Yutaka Kinoshita and Kaori Yoshida },
    BOOKTITLE = { 2008 IEEE Congress on Evolutionary Computation (CEC08?), Proceedings },
    ADDED = { 2008-06-30 16:00:18 +0900 },
    MODIFIED = { 2010-09-24 18:24:09 +0900 },
    PAGES = { 3536-3542 },
    TITLE = { Auxiliary Objectives for the Evolutionary Multi-Objective Principal Color Extraction from Logo Images },
    YEAR = { 2008 },
}