Results 1 - 10
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935
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
- 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 ..."
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Cited by 1923 (47 self)
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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
"... 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
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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
- 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, ..."
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Cited by 1 (1 self)
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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
"... 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 ..."
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Cited by 7 (1 self)
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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
- 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 ..."
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Cited by 266 (14 self)
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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
- 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 ..."
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Cited by 55 (9 self)
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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
- 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 ..."
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Cited by 507 (15 self)
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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
- 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 ..."
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Cited by 133 (25 self)
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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
- 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 ..."
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Cited by 443 (20 self)
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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
- 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 " ..."
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Cited by 3283 (9 self)
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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
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