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» Optimizing Learning in Image Retrieval
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MM
2009
ACM
209views Multimedia» more  MM 2009»
14 years 3 months ago
Brain state decoding for rapid image retrieval
Human visual perception is able to recognize a wide range of targets under challenging conditions, but has limited throughput. Machine vision and automatic content analytics can p...
Jun Wang, Eric Pohlmeyer, Barbara Hanna, Yu-Gang J...
MIR
2005
ACM
137views Multimedia» more  MIR 2005»
14 years 2 months ago
A content-based image retrieval system for fish taxonomy
It is estimated that less than ten percent of the world’s species have been discovered and described. The main reason for the slow pace of new species description is that the sc...
Yixin Chen, Henry L. Bart Jr., Fei Teng
ICASSP
2007
IEEE
14 years 3 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
SAC
2004
ACM
14 years 2 months ago
Integrating similarity-based queries in image DBMSs
Until recently, issues in image retrieval have been handled in DBMSs and in computer vision as separate research works. Nowadays, the trend is towards integrating the two approach...
Solomon Atnafu, Richard Chbeir, David Coquil, Lion...
ICCV
1998
IEEE
14 years 1 months ago
Condensing Image Databases when Retrieval is Based on Non-Metric Distances
One of the key problems in appearance-based vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performa...
David W. Jacobs, Daphna Weinshall, Yoram Gdalyahu