Emotion-Based Textile Indexing Using Colors and Texture

For a given product or object, predicting human emotions is very important in many business, scientific and engineering applications. There has been a significant amount of research work on the image-based analysis of human emotions in a number of research areas because human emotions are usually dependent on human vision. However, there has been little research on the computer image processing-based prediction, although such approach is naturally very appealing. In this paper, we discuss challenging issues in how to index images based on human emotions and present a heuristic approach to emotion-based image indexing. The effectiveness of image features such as colors, textures, and objects (or shapes) varies significantly depending on the types of emotion or image data. Therefore, we propose adaptive and selective techniques. With respect to six adverse pairs of emotions such as weak-strong, we evaluated the effectiveness of those techniques by applying them to the set of about 160 images in a commercial curtain pattern book obtained from the Dongdaemoon textile shopping mall in Seoul. Our preliminary experimental results showed that the proposed adaptive and selective strategies are effective and improve the accuracy of indexing significantly depending on the type of emotion.