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» Textural Features and Relevance Feedback for Image Retrieval
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MIR
2006
ACM
145views Multimedia» more  MIR 2006»
15 years 5 months ago
Similarity learning via dissimilarity space in CBIR
In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the fe...
Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeu...
IBPRIA
2005
Springer
15 years 5 months ago
Dynamic Texture Recognition Using Normal Flow and Texture Regularity
The processing, description and recognition of dynamic (time-varying) textures are new exciting areas of texture analysis. Many real-world textures are dynamic textures whose retri...
Renaud Péteri, Dmitry Chetverikov
ICPR
2000
IEEE
16 years 25 days ago
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective technique for adaptively computing local feature relevance for content-based image retrieval. It however becomes le...
Douglas R. Heisterkamp, Jing Peng, H. K. Dai
ICCV
2007
IEEE
15 years 6 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...
VISAPP
2007
15 years 28 days ago
Texture based image indexing and retrieval
The Content Based Image Retrieval (CBIR) has been an active research area. Given a collection of images, it is to retrieve the images based on a query image, which is specified by...
N. Gnaneswara Rao, V. Vijaya Kumar