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ICCV
2011
IEEE
12 years 7 months ago
HEAT: Iterative Relevance Feedback with One Million Images
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hu...
Nicolae Suditu, Francois Fleuret
PAKDD
2009
ACM
263views Data Mining» more  PAKDD 2009»
14 years 2 months ago
Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
It is a challenging and important task to retrieve images from a large and highly varied image data set based on their visual contents. Problems like how to fill the semantic gap b...
Xin Chen, Xiaohua Hu, Xiajiong Shen
CISST
2004
164views Hardware» more  CISST 2004»
13 years 9 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
SPIESR
2001
141views Database» more  SPIESR 2001»
13 years 9 months ago
Image retrieval based on region shape similarity
This paper presents an image retrieval method based on region shape similarity. In our approach, we first segment images into primitive regions and then combine some of the primit...
Cheng Chang, Wenyin Liu, HongJiang Zhang
ICPR
2006
IEEE
14 years 8 months ago
Texture Image Retrieval Using Novel Non-separable Filter Banks Based on Centrally Symmetric Matrices
Though millions of images are stored in a large digital image library today, the user can not access or make full use of these image information unless the digital image library i...
Patrick Shen-Pei Wang, Xinge You, Yuan Yan Tang, Y...