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» Optimizing Learning in Image Retrieval
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AAAI
2007
15 years 5 days ago
Semi-Supervised Learning with Very Few Labeled Training Examples
In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled e...
Zhi-Hua Zhou, De-Chuan Zhan, Qiang Yang
ICPR
2008
IEEE
15 years 4 months ago
Semi-supervised learning by locally linear embedding in kernel space
Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully specified by an Euclidean distance based local ne...
Rujie Liu, Yuehong Wang, Takayuki Baba, Daiki Masu...
ECCV
2008
Springer
15 years 11 months ago
Feature Correspondence Via Graph Matching: Models and Global Optimization
Abstract. In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter an...
Lorenzo Torresani, Vladimir Kolmogorov, Carsten Ro...
SIGIR
2005
ACM
15 years 3 months ago
Linear discriminant model for information retrieval
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
Jianfeng Gao, Haoliang Qi, Xinsong Xia, Jian-Yun N...
CVPR
2011
IEEE
14 years 6 months ago
Iterative Quantization: A Procrustean Approach to Learning Binary Codes
This paper addresses the problem of learning similaritypreserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient altern...
Yunchao Gong, Svetlana Lazebnik