Results 1 - 10 of 935

Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories

by Cordelia Schmid - In CVPR
"... This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial pyrami ..."
Abstract - Cited by 1923 (47 self) - Add to MetaCart
pyramid ” is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

by unknown authors
"... This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial pyrami ..."
Abstract - Add to MetaCart
pyramid ” is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database

A pyramid nearest neighbor search kernel for object categorization

by Hong Cheng, Rongchao Yu, Zicheng Liu, Yiguang Liu - in IEEE ICPR , 2012
"... Nearest-Neighbor based Image Classification (N-NIC) has drawn considerable attention in the past sev-eral years because it does not require classifier training. Similar to an orderless Bag-of-Feature image represen-tation, the traditional NNIC ignores global geometric correspondence. In this paper, ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Nearest-Neighbor based Image Classification (N-NIC) has drawn considerable attention in the past sev-eral years because it does not require classifier training. Similar to an orderless Bag-of-Feature image represen-tation, the traditional NNIC ignores global geometric correspondence. In this paper

Spatial Weighting for Bag-of-Features

by Marcin Marszałek, Cordelia Schmid
"... This paper presents an extension to category classification with bag-of-features, which represents an image as an orderless distribution of features. We propose a method to exploit spatial relations between features by utilizing object boundaries provided during supervised training. We boost the wei ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
This paper presents an extension to category classification with bag-of-features, which represents an image as an orderless distribution of features. We propose a method to exploit spatial relations between features by utilizing object boundaries provided during supervised training. We boost

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

Packing bag-of-features

by Hervé Jégou, Matthijs Douze, Cordelia Schmid - in ICCV , 2009
"... One of the main limitations of image search based on bag-of-features is the memory usage per image. Only a few million images can be handled on a single machine in reasonable response time. In this paper, we first evaluate how the memory usage is reduced by using lossless index compression. We then ..."
Abstract - Cited by 55 (9 self) - Add to MetaCart
then propose an approximate representation of bag-of-features obtained by projecting the corresponding histogram onto a set of pre-defined sparse projection functions, producing several image descriptors. Coupled with a proper indexing structure, an image is represented by a few hundred bytes. A distance

Image retrieval: Current techniques, promising directions and open issues

by Yong Rui, Thomas S. Huang - Journal of Visual Communication and Image Representation , 1999
"... This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image fea ..."
Abstract - Cited by 507 (15 self) - Add to MetaCart
feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified

Improving bag-of-features for large scale image search

by Hervé Jégou, Matthijs Douze, Cordelia Schmid - International Journal of Computer Vision
"... This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constra ..."
Abstract - Cited by 133 (25 self) - Add to MetaCart
This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency

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

Rapid object detection using a boosted cascade of simple features

by Paul Viola, Michael Jones - ACCEPTED CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2001 , 2001
"... This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the " ..."
Abstract - Cited by 3283 (9 self) - Add to MetaCart
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called
Results 1 - 10 of 935