Color texture retrieval using the collective color texture model
Umasankar Kandaswamy, Donald Adjeroh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.