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» Learning the Relative Importance of Features in Image Data
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141
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PAMI
2011
14 years 10 months ago
Color Constancy Using Natural Image Statistics and Scene Semantics
—Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be conside...
Arjan Gijsenij, Theo Gevers
129
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ACL
2003
15 years 5 months ago
Syntactic Features and Word Similarity for Supervised Metonymy Resolution
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...
Malvina Nissim, Katja Markert
137
Voted
MLDM
1999
Springer
15 years 7 months ago
Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval
In large content-based image database applications, e cient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques f...
Irwin King, Tak-Kan Lau
163
Voted
PAMI
2012
13 years 6 months ago
IntentSearch: Capturing User Intention for One-Click Internet Image Search
—Web-scale image search engines (e.g. Google Image Search, Bing Image Search) mostly rely on surrounding text features. It is difficult for them to interpret users’ search int...
Xiaoou Tang, Ke Liu, Jingyu Cui, Fang Wen, Xiaogan...
CIKM
2009
Springer
15 years 10 months ago
A social recommendation framework based on multi-scale continuous conditional random fields
This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social recommendation emphasizes utilizing various attributes informatio...
Xin Xin, Irwin King, Hongbo Deng, Michael R. Lyu