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» Optimizing Learning in Image Retrieval
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ICCV
2007
IEEE
14 years 3 months ago
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...
CIVR
2006
Springer
219views Image Analysis» more  CIVR 2006»
14 years 23 days ago
Bayesian Learning of Hierarchical Multinomial Mixture Models of Concepts for Automatic Image Annotation
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
Rui Shi, Tat-Seng Chua, Chin-Hui Lee, Sheng Gao
KDD
2010
ACM
249views Data Mining» more  KDD 2010»
13 years 11 months ago
Semi-supervised sparse metric learning using alternating linearization optimization
In plenty of scenarios, data can be represented as vectors mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining app...
Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, ...
ACCV
2010
Springer
13 years 4 months ago
Optimizing Visual Vocabularies Using Soft Assignment Entropies
The state of the art for large database object retrieval in images is based on quantizing descriptors of interest points into visual words. High similarity between matching image r...
Yubin Kuang, Kalle Åström, Lars Kopp, M...
CVPR
2007
IEEE
14 years 11 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen