Trace Transform Based Method for Color Image Domain Identification
Egileak: Igor G. Olaizola, Marco Quartulli, Julián Flórez, Basilio Sierra
Data: 01.04.2014
IEEE Transactions on Multimedia
Abstract
Context categorization is a fundamental pre-requisite for multi-domain multimedia content analysis applications. Most feature extraction methods require prior knowledge to decide if they are suitable for a specific domain and to optimize their input parameters. In this paper, we introduce a new color image context categorization method (DITEC) based on the trace transform. The problem of dimensionality reduction of the obtained trace transform signal is addressed through statistical descriptors of its frequency representation that keep the underlying information. We also analyze the distortions produced by the parameters that determine the sampling of the discrete trace transform. Moreover, Feature Subset Selection (FSS) is applied to both, improve the classification performance and compact the final length of the descriptor that will be provided to the classifier. These extracted features offer a highly discriminant behovior for content categorization without prior knowledge requirements. The method has been experimentally validated through two different datasets.
BIB_text
author = {Igor G. Olaizola, Marco Quartulli, Julián Flórez, Basilio Sierra},
title = {Trace Transform Based Method for Color Image Domain Identification},
journal = {IEEE Transactions on Multimedia},
pages = {679-685},
number = {3},
volume = {16},
keywds = {
CBIR, image domain identification, pattern recognition, trace transform
}
abstract = {
Context categorization is a fundamental pre-requisite for multi-domain multimedia content analysis applications. Most feature extraction methods require prior knowledge to decide if they are suitable for a specific domain and to optimize their input parameters. In this paper, we introduce a new color image context categorization method (DITEC) based on the trace transform. The problem of dimensionality reduction of the obtained trace transform signal is addressed through statistical descriptors of its frequency representation that keep the underlying information. We also analyze the distortions produced by the parameters that determine the sampling of the discrete trace transform. Moreover, Feature Subset Selection (FSS) is applied to both, improve the classification performance and compact the final length of the descriptor that will be provided to the classifier. These extracted features offer a highly discriminant behovior for content categorization without prior knowledge requirements. The method has been experimentally validated through two different datasets.
}
isi = {1},
date = {2014-04-01},
year = {2014},
}
