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CVPR
2011
IEEE
9 years 6 months ago
Kernelized Structural SVM Learning for Supervised Object Segmentation
Object segmentation needs to be driven by top-down knowledge to produce semantically meaningful results. In this paper, we propose a supervised segmentation approach that tightly ...
Luca Bertelli, Tianli Yu, Diem Vu, Salih Gokturk
ICCV
2007
IEEE
10 years 9 months ago
Support Kernel Machines for Object Recognition
Kernel classi´Čüers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by...
Ankita Kumar, Cristian Sminchisescu
CVPR
2008
IEEE
10 years 4 months ago
Loose shape model for discriminative learning of object categories
We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent ...
Margarita Osadchy, Elran Morash
IJCV
2008
266views more  IJCV 2008»
10 years 2 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
ICCV
2005
IEEE
11 years 4 months ago
A Supervised Learning Framework for Generic Object Detection in Images
In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...
Saad Ali, Mubarak Shah
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