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» Local Image Descriptors Using Supervised Kernel ICA
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PSIVT
2009
Springer
400views Multimedia» more  PSIVT 2009»
13 years 10 months ago
Local Image Descriptors Using Supervised Kernel ICA
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Masaki Yamazaki, Sidney Fels
BMVC
2010
13 years 1 months ago
Weakly Supervised Object Recognition and Localization with Invariant High Order Features
High order features have been proposed to incorporate geometrical information into the "bag of feature" representation. We propose algorithms to perform fast weakly supe...
Yimeng Zhang, Tsuhan Chen
CVPR
2010
IEEE
13 years 8 months ago
The Automatic Design of Feature Spaces for Local Image Descriptors using an Ensemble of Non-linear Feature Extractors
The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classifi...
Gustavo Carneiro
ECCV
2004
Springer
14 years 5 months ago
Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis
Abstract. We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the ...
Jason Meltzer, Ming-Hsuan Yang, Rakesh Gupta, Stef...
MM
2010
ACM
462views Multimedia» more  MM 2010»
13 years 3 months ago
KPB-SIFT: a compact local feature descriptor
Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of ...
Gangqiang Zhao, Ling Chen, Gencai Chen, Junsong Yu...