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» Image Classification Using Marginalized Kernels for Graphs
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ICCV
2009
IEEE
1022views Computer Vision» more  ICCV 2009»
16 years 4 months ago
Kernelized Locality-Sensitive Hashing for Scalable Image Search
Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance fun...
Brian Kulis, Kristen Grauman
83
Voted
ICPR
2010
IEEE
14 years 9 months ago
Improving Classification Accuracy by Comparing Local Features through Canonical Correlations
Classifying images using features extracted from densely sampled local patches has enjoyed significant success in many detection and recognition tasks. It is also well known that ...
Mert Dikmen, Thomas S. Huang
CVPR
2008
IEEE
16 years 1 months ago
Relaxed matching kernels for robust image comparison
The popular bag-of-features representation for object recognition collects signatures of local image patches and discards spatial information. Some have recently attempted to at l...
Andrea Vedaldi, Stefano Soatto
124
Voted
SCALESPACE
2007
Springer
15 years 5 months ago
Uniform and Textured Regions Separation in Natural Images Towards MPM Adaptive Denoising
Abstract. Natural images consist of texture, structure and smooth regions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In...
Noura Azzabou, Nikos Paragios, Frederic Guichard
ICML
2007
IEEE
16 years 16 days ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...