Paper
16 November 2005 Color texture retrieval using the collective color texture model
Umasankar Kandaswamy, Donald Adjeroh
Author Affiliations +
Proceedings Volume 5999, Intelligent Systems in Design and Manufacturing VI; 59990W (2005) https://doi.org/10.1117/12.632590
Event: Optics East 2005, 2005, Boston, MA, United States
Abstract
Color and texture has been extensively studied in the field of image processing and computer vision. Industrial applications based on computer vision that uses color and texture information for produce recognition and surface change detection are more common these days. Even though color and texture have been individually studied and used for retrieval and classification purposes, very little work has been done in the problem of effective integration of color and texture information. Previously, we proposed the Collective Color Texture (CCT) model that functionally considers both the color and texture outcomes and generates an effective descriptor for the color texture. We showed that the CCT model outperformed other common integration methods when used for supervised classification. In this work, we use the CCT model for texture retrieval using histogram based color representation, various texture based representations. We used Outex 13 database for our experiments since it has wide variety of color textures (such as granite, canvas, carpet, etc) that are commonly present in industrial applications. We compare retrieval performance using individual methods with those from commonly used integrated techniques. Our results show that the CCT model provides an overall superior retrieval performance when compared with other popular approaches.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Umasankar Kandaswamy and Donald Adjeroh "Color texture retrieval using the collective color texture model", Proc. SPIE 5999, Intelligent Systems in Design and Manufacturing VI, 59990W (16 November 2005); https://doi.org/10.1117/12.632590
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Cited by 1 scholarly publication.
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KEYWORDS
Feature extraction

Image retrieval

Databases

Computer vision technology

Image classification

Machine vision

Distance measurement

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