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KANSEI-BASED IMAGE RETRIEVAL WITH BAYESIAN DECISION MODELS AND RELEVANCE FEEDBACK
2008
Kansei Engineering International
The Kansei-based image retrieval (KBIR) aims to index images based on user's emotion and sensation. In this paper, we construct a KBIR system using scenery images as retrieval objects, which consists of three parts: visual features extraction, Kansei perception inference, and retrieval adjustment by relevance feedback. In the first part, low-level visual features such as color, texture, and shape features are extracted from perceptual viewpoints for all images. In addition, 5 pairs of Kansei
doi:10.5057/kei.7.171
fatcat:gde2karnrzdblenifaspx6gr2u