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Results 1 - 10
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2,226
Visual categorization with bags of keypoints
- In Workshop on Statistical Learning in Computer Vision, ECCV
, 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
Abstract
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Cited by 1005 (14 self)
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Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors
Empowering Visual Categorization With the GPU
, 2011
"... Visual categorization is important to manage large collections of digital images and video, where textual metadata is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe ..."
Abstract
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Cited by 22 (7 self)
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Visual categorization is important to manage large collections of digital images and video, where textual metadata is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe
Accelerating Visual Categorization with the GPU
, 2010
"... Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a sever ..."
Abstract
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Cited by 1 (1 self)
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Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a
Adapted vocabularies for generic visual categorization
- In ECCV
, 2006
"... Abstract. Several state-of-the-art Generic Visual Categorization (GVC) systems are built around a vocabulary of visual terms and characterize images with one histogram of visual word counts. We propose a novel and practical approach to GVC based on a universal vocabulary, which describes the content ..."
Abstract
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Cited by 108 (5 self)
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Abstract. Several state-of-the-art Generic Visual Categorization (GVC) systems are built around a vocabulary of visual terms and characterize images with one histogram of visual word counts. We propose a novel and practical approach to GVC based on a universal vocabulary, which describes
Visual categorization with aerial photographs
- Annals of the Association of American Geographers
, 2002
"... This article investigates how people process information from aerial photographs to categorize locations. Three cognitive experiments were conducted with human subjects viewing a series of aerial photographs and categorizing the land use for target locations. Reaction time, accuracy, and confidence ..."
Abstract
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Cited by 5 (1 self)
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were considered as dependent variables related to the success of the categorization process. The first experiment considered two categories of land use, the relative size of the visual field, and two rounds of unsupervised learning. Subjects were more successful categorizing higher-order land
Social negative bootstrapping for visual categorization
- In ICMR
, 2011
"... To learn classifiers for many visual categories, obtaining labeled training examples in an efficient way is crucial. Since a classifier tends to misclassify negative examples which are visually similar to positive examples, inclusion of such informative negatives should be stressed in the learning p ..."
Abstract
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Cited by 10 (7 self)
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. On a popular visual categorization benchmark our precision at 20 increases by 34%, compared to baselines trained on randomly sampled negatives. We achieve more accurate visual categorization without the need of manually labeling any negatives.
Visual Categorization with Negative Examples for Free
"... Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is becoming a novel yet subjective and inaccurate source of learning examples. Different from existing work focusing on coll ..."
Abstract
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Cited by 5 (3 self)
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Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is becoming a novel yet subjective and inaccurate source of learning examples. Different from existing work focusing
Bootstrapping Visual Categorization with Relevant Negatives
"... Abstract—Learning classifiers for many visual concepts is important for image categorization and retrieval. As a classifier tends to misclassify negative examples which are visually similar to positive ones, inclusion of such misclassified and thus relevant negatives should be stressed during learni ..."
Abstract
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Cited by 9 (3 self)
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Abstract—Learning classifiers for many visual concepts is important for image categorization and retrieval. As a classifier tends to misclassify negative examples which are visually similar to positive ones, inclusion of such misclassified and thus relevant negatives should be stressed during
Learning and using taxonomies for fast visual categorization
- In CVPR
"... The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously Ncat = 104−105 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classific ..."
Abstract
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Cited by 56 (3 self)
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The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously Ncat = 104−105 visual categories requires sub-linear classification costs. We explore algorithms for automatically building
Comparing compact codebooks for visual categorization
- COMPUTER VISION AND IMAGE UNDERSTANDING 114 (2010) 450–462
, 2010
"... ..."
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