Direct spatial search on pictorial databases using packed R-trees
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Content-Based Image and Video Retrieval
Content-Based Image and Video Retrieval
Digital Image Processing
An Intelligent Image Database System
IEEE Transactions on Software Engineering
Automatic textile image annotation by predicting emotional concepts from visual features
Image and Vision Computing
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
Emotion-based textile indexing using neural networks
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
SAMMI: semantic affect-enhanced multimedia indexing
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Towards multimodal emotion recognition: a new approach
Proceedings of the ACM International Conference on Image and Video Retrieval
The affective experience of handling digital fabrics: tactile and visual cross-modal effects
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Emotion based classification of natural images
Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web
Emotion-based textile indexing using colors, texture and patterns
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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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.