Results 1 - 10 of 511

Recognition with Local Features: The Kernel Recipe

by Christian Wallraven , 2003
"... Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two ..."
Abstract - Cited by 178 (26 self) - Add to MetaCart
Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine

A sparse object category model for efficient learning and exhaustive recognition

by Rob Fergus, Pietro Perona, Andrew Zisserman - In CVPR , 2005
"... We present a “parts and structure ” model for object category recognition that can be learnt efficiently and in a weakly-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representat ..."
Abstract - Cited by 168 (10 self) - Add to MetaCart
efficiently in a complete manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image. 1

Style-based classification of Chinese ink and wash paintings

by Jiachuan Sheng, Jianmin Jiang, Jiachuan Sheng, Jianmin Jiang
"... Abstract. Following the fact that a large collection of ink and wash paint-ings (IWP) is being digitized and made available on the Internet, their auto-mated content description, analysis, and management are attracting attention across research communities. While existing research in relevant areas ..."
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for the multiple neural network classification results in which the entropy is used as a pointer to combine the global and local features. Evaluations via experiments support that the proposed algorithm

Region classification with markov field aspect models

by Jakob Verbeek, Bill Triggs - In CVPR , 2007
"... Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-offeature approaches label each pixel or patch independently. More advanced models attempt to improve the coherence of the labellings by introducing some form of inter-patch coupling: traditi ..."
Abstract - Cited by 97 (14 self) - Add to MetaCart
: traditional spatial models such as MRF’s provide crisper local labellings by exploiting neighbourhoodlevel couplings, while aspect models such as PLSA and LDA use global relevance estimates (global mixing proportions for the classes appearing in the image) to shape the local choices. We point out that the two

Cast shadow removal combining local and global features

by Zhou Liu, Kaiqi Huang, Tieniu Tan, Liangsheng Wang - The Seventh International Workshop on Visual Surveillance , 2007
"... In this paper, we present a method using pixel-level in-formation, local region-level information and global-level information to remove shadow. At the pixel-level, we em-ploy GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex il-lumination c ..."
Abstract - Cited by 16 (2 self) - Add to MetaCart
In this paper, we present a method using pixel-level in-formation, local region-level information and global-level information to remove shadow. At the pixel-level, we em-ploy GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex il

COMBINING CLASSIFICATIONS BASED ON LOCAL AND GLOBAL FEATURES: APPLICATION TO SINGER IDENTIFICATION

by Lise Regnier, Geoffroy Peeters
"... In this paper we investigate the problem of singer identification on acapella recordings of isolated notes. Most of studies on singer identification describe the content of signals of singing voice with features related to the timbre (such as MFCC or LPC). These features aim to describe the behavior ..."
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accurate than timbre-based features. We propose to increase the recognition rate of singer identification by combining information conveyed by local and global description of notes. The proposed method, that shows good results, can be adapted for classification problem involving a large number of classes

A completed modeling of local binary pattern operator for texture classification

by Zhenhua Guo, Lei Zhang, David Zhang - IEEE Trans. Image Processing , 2010
"... Abstract—In this paper, a completed modeling of the LBP operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent t ..."
Abstract - Cited by 73 (6 self) - Add to MetaCart
_M), are proposed to code them. The traditional LBP is equivalent to the CLBP_S part of CLBP, and we show that CLBP_S preserves more information of the local structure than CLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBP_S, CLBP_M, and CLBP

A Comparative Study about Object Classification Based On Global and Local Features

by Hammad Naeem , Maria Minhas , Jameel Ahmed
"... Abstract Scene classification and object recognition is a hot area of research in the field of computer vision and has always fascinated researchers to explore strategies for optimization of results. Global and local features are manipulated to find a match in the images or scene categories. This p ..."
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Abstract Scene classification and object recognition is a hot area of research in the field of computer vision and has always fascinated researchers to explore strategies for optimization of results. Global and local features are manipulated to find a match in the images or scene categories

Combining sorted random features for texture classification

by Li Liu, Paul Fieguth, Gangyao Kuang - in Proc. 18th IEEE Int. Conf. Image Process. (ICIP , 2011
"... This paper explores the combining of powerful local texture descrip-tors and the advantages over single descriptors for texture classifi-cation. The proposed system is composed of three components: (i) highly discriminative and robust sorted random projections (SRP) features; (ii) a global Bag-of-Wo ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
This paper explores the combining of powerful local texture descrip-tors and the advantages over single descriptors for texture classifi-cation. The proposed system is composed of three components: (i) highly discriminative and robust sorted random projections (SRP) features; (ii) a global Bag

Combine local and global features for image segmentation using iterative classification and region merging

by Qiyao Yu, David A. Clausi - Proc. 2 nd Canadian Conf. on Computer and Robot Vision , 2005
"... In MRF based unsupervised segmentation, the MRF model parameters are typically estimated globally. Those global statistics sometimes are far from accurate for lo-cal areas if the image is highly non-stationary, and hence will generate false boundaries. The problem cannot be solved if local statistic ..."
Abstract - Cited by 3 (3 self) - Add to MetaCart
statistics are not considered. This work in-corporates the local feature of edge strength in the MRF energy function, and segmentation is obtained by reduc-ing the energy function using iterative classification and region merging.
Results 1 - 10 of 511