Trace Transform Based Method for Color Image Domain Identification

• Published in: IEEE transactions on multimedia Vol. 16; no. 3; pp. 679 - 685
• Main Authors: Olaizola, Igor G., Quartulli, Marco, Florez, Julian, Sierra, Basilio
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; CBIR; Classification; Color; Computer science; control theory; systems; Context; Data processing. List processing. Character string processing; Discrete cosine transforms; Distortion; Exact sciences and technology; Feature extraction; Image color analysis; image domain identification; Image resolution; Memory organisation. Data processing; Multimedia; pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Representations; Sampling; Semantics; Software; trace transform; Transforms; Multimedia; Statistical analysis; Image processing; image domain identification; Discrete transformation; Pattern recognition; Context aware; Color image; Dimension reduction; Classification; CBIR; Selection criterion; Feature extraction; Content-based retrieval; Sampling; Categorization; Pattern extraction; trace transform; Content analysis
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2014.2300843

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 behavior for content categorization without prior knowledge requirements. The method has been experimentally validated through two different datasets.