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CVPR
2007
IEEE
16 years 8 months ago
Feature Mining for Image Classification
The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense...
Piotr Dollár, Zhuowen Tu, Hai Tao, Serge Be...
ECCV
2004
Springer
16 years 8 months ago
Transformation-Invariant Embedding for Image Analysis
Abstract. Dimensionality reduction is an essential aspect of visual processing. Traditionally, linear dimensionality reduction techniques such as principle components analysis have...
Ali Ghodsi, Jiayuan Huang, Dale Schuurmans
GIS
2009
ACM
16 years 7 months ago
Sea Floor Bathymetry Trackline Surface Fitting Without Visible Artifacts Using ODETLAP
High quality, artifact-free fitting a bathymetry (sea-floor) surface to very unevenly spaced depth data from ship tracklines is possible with ODETLAP (Overdetermined Laplacian Par...
Tsz-Yam Lau, W. Randolph Franklin, You Li, Zhongyi...
KDD
2007
ACM
160views Data Mining» more  KDD 2007»
16 years 7 months ago
Show me the money!: deriving the pricing power of product features by mining consumer reviews
The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumergenerated product reviews have become a valuable source of inf...
Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeir...
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 7 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson