Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Retrieval by Ontological Description of Shapes (IRONS), Early Results
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Matching colors with KANSEI vocabulary using similarity measure based on wordnet
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Kansei Processing Agent for Personalizing Retrieval
UM '07 Proceedings of the 11th international conference on User Modeling
Trajectory Annotation and Retrieval Based on Semantics
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Extended spatio-temporal relations between moving and non-moving objects
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
Emotion based classification of natural images
Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web
Adaptive-Tangent space representation for image retrieval based on kansei
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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Recently the demand for image retrieval and recognizable extraction corresponding to KANSEI (sensibility) has been increasing, and the studies focused on establishing those KANSEI-based systems have been progressing more than ever. In addition, the attempt to understand, measure and evaluate, and apply KANSEI to situational design or products will be required more and more in the future. Particularly, study of KANSEI-based image retrieval tools have especially been in the spotlight. So many investigators give a trial of using KANSEI for image retrieval. However, the research in this area is still under its primary stage because it is difficult to process higher-level contents as emotion or KANSEI of human. To solve this problem, we suggest the KANSEI-Vocabulary Scale by associating human sensibilities with shapes among visual information. And we construct the object retrieval system for evaluation of KANSEI-Vocabulary Scale by shape. In our evaluation results, we are able to retrieve object images with the most appropriate shape in term of the query's KANSEI. Furthermore, the method achieves an average rate of 71% user's satisfaction.