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ICCV
2007
IEEE
14 years 6 months ago
Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval
Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of quest...
Hichem Sahbi, Jean-Yves Audibert, Renaud Keriven
SPIESR
1998
195views Database» more  SPIESR 1998»
13 years 6 months ago
Relevance Feedback Techniques in Interactive Content-Based Image Retrieval
Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many system...
Yong Rui, Thomas S. Huang, Sharad Mehrotra
ICASSP
2007
IEEE
13 years 11 months ago
Integrating Relevance Feedback in Boosting for Content-Based Image Retrieval
Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In thi...
Jie Yu, Yijuan Lu, Yuning Xu, Nicu Sebe, Qi Tian
CIVR
2005
Springer
156views Image Analysis» more  CIVR 2005»
13 years 10 months ago
Content-Based Object Movie Retrieval by Use of Relevance Feedback
Abstract. Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie is a good representation of a physical object because it can ...
Li-Wei Chan, Cheng-Chieh Chiang, Yi-Ping Hung
CISST
2004
164views Hardware» more  CISST 2004»
13 years 6 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp