Results 1 - 10 of 19,182

Locality-constrained linear coding for image classification

by Jinjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv, Thomas Huang, Yihong Gong - IN: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN CLASSIFICATOIN , 2010
"... The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC util ..."
Abstract - Cited by 443 (20 self) - Add to MetaCart
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC

Linear spatial pyramid matching using sparse coding for image classification

by Jianchao Yang, Kai Yu, Yihong Gong, Thomas Huang - in IEEE Conference on Computer Vision and Pattern Recognition(CVPR , 2009
"... Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algo ..."
Abstract - Cited by 497 (21 self) - Add to MetaCart
Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup

Improving the Fisher kernel for large-scale image classification.

by Florent Perronnin , Jorge Sánchez , Thomas Mensink - In ECCV, , 2010
"... Abstract. The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enric ..."
Abstract - Cited by 362 (20 self) - Add to MetaCart
Abstract. The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice

Indoor-outdoor image classification

by Martin Szummer, Rosalind W. Picard - IN IEEE INTL. WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO DATABASES , 1998
"... We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model ..."
Abstract - Cited by 269 (0 self) - Add to MetaCart
We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model

In Defense of Nearest-Neighbor Based Image Classification

by Oren Boiman
"... State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between th ..."
Abstract - Cited by 266 (2 self) - Add to MetaCart
State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between

IMAGE CLASSIFICATION

by unknown authors
"... In pursuance of the formalizing the methodology of the present work, a substantial amount of the literature was surveyed. Besides, some survey papers regarding image classification. Literature related to the topic, having contributed to the present methodology has been categorized into the subtopics ..."
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In pursuance of the formalizing the methodology of the present work, a substantial amount of the literature was surveyed. Besides, some survey papers regarding image classification. Literature related to the topic, having contributed to the present methodology has been categorized

Simultaneous Image Classification and Annotation

by Chong Wang, David Blei, Li Fei-fei
"... Image classification and annotation are important problems in computer vision, but rarely considered together. Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated with wo ..."
Abstract - Cited by 148 (7 self) - Add to MetaCart
Image classification and annotation are important problems in computer vision, but rarely considered together. Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated

Sampling strategies for bag-of-features image classification

by Eric Nowak, Frédéric Jurie, Bill Triggs - In Proc. ECCV , 2006
"... Abstract. Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance. They evolved from texton methods in texture analysis. The basic idea is to treat images as loose collections of independent patches, sampling ..."
Abstract - Cited by 266 (14 self) - Add to MetaCart
Abstract. Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance. They evolved from texton methods in texture analysis. The basic idea is to treat images as loose collections of independent patches, sampling

Support-vector machines for histogram-based image classification

by Olivier Chapelle, Patrick Haffner, Vladimir N. Vapnik - IEEE Transactions on Neural Networks , 1999
"... Abstract — Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM’s) can generalize well on difficult image classification problems where the only features are ..."
Abstract - Cited by 229 (1 self) - Add to MetaCart
Abstract — Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM’s) can generalize well on difficult image classification problems where the only features

Image classification for content-based indexing

by Aditya Vailaya, Mário A. T. Figueiredo, Anil K. Jain, Hong-jiang Zhang - IEEE TRANSACTIONS ON IMAGE PROCESSING , 2001
"... Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the ..."
Abstract - Cited by 227 (2 self) - Add to MetaCart
that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified
Results 1 - 10 of 19,182