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» Learning Mid-Level Features For Recognition
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DICTA
2003
13 years 9 months ago
Learning Semantic Concepts from Visual Data Using Neural Networks
For content-based image retrieval techniques, query image is used to pick up and rank some relevant images from a database using some certain similarity metric. If semantic feature...
Xiaohang Ma, Dianhui Wang
CSL
2010
Springer
13 years 7 months ago
Improving supervised learning for meeting summarization using sampling and regression
Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this paper, we focus on ext...
Shasha Xie, Yang Liu
ICCV
2005
IEEE
14 years 9 months ago
Building a Classification Cascade for Visual Identification from One Example
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
TKDE
2008
152views more  TKDE 2008»
13 years 7 months ago
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by max...
Deng Cai, Xiaofei He, Jiawei Han
RSFDGRC
2005
Springer
190views Data Mining» more  RSFDGRC 2005»
14 years 1 months ago
Finding Rough Set Reducts with SAT
Abstract. Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine...
Richard Jensen, Qiang Shen, Andrew Tuson