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» Features for Image Retrieval: A Quantitative Comparison
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ICPR
2008
IEEE
14 years 1 months ago
A non-parametric scale-based corner detector
This paper introduces a new Harris-affine corner detector algorithm, that does not need parameters to locate corners in images, given an observation scale. Standard detectors req...
Fabio Bellavia, Domenico Tegolo, Cesare Valenti
CIVR
2006
Springer
172views Image Analysis» more  CIVR 2006»
13 years 11 months ago
Retrieving Shapes Efficiently by a Qualitative Shape Descriptor: The Scope Histogram
Abstract. Efficient image retrieval from large image databases is a challenging problem. In this paper we present a method offering constant time complexity for the comparison of t...
Arne Schuldt, Björn Gottfried, Otthein Herzog
MM
2005
ACM
171views Multimedia» more  MM 2005»
14 years 1 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
ICDAR
2009
IEEE
13 years 5 months ago
Handwritten Word Image Retrieval with Synthesized Typed Queries
We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained "on the fly...
José A. Rodríguez-Serrano, Florent P...
CVPR
1997
IEEE
1944views Computer Vision» more  CVPR 1997»
14 years 9 months ago
Image Indexing Using Color Correlograms
We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effec...
Jing Huang, Ravi Kumar, Mandar Mitra, Wei-Jing Zhu...