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Blobworld: Image segmentation using expectation-maximization and its application to image querying (2002)

by C Carson, S Belongie, H Greenspan, J Malik
Venue:PAMI
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Matching words and pictures

by Kobus Barnard, Pinar Duygulu, David Forsyth, Nando De Freitas, David M. Blei, Michael I. Jordan - JOURNAL OF MACHINE LEARNING RESEARCH , 2003
"... We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation ..."
Abstract - Cited by 665 (40 self) - Add to MetaCart
We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation) and corresponding to particular image regions (region naming). Auto-annotation might help organize and access large collections of images. Region naming is a model of object recognition as a process of translating image regions to words, much as one might translate from one language to another. Learning the relationships between image regions and semantic correlates (words) is an interesting example of multi-modal data mining, particularly because it is typically hard to apply data mining techniques to collections of images. We develop a number of models for the joint distribution of image regions and words, including several which explicitly learn the correspondence between regions and words. We study multi-modal and correspondence extensions to Hofmann’s hierarchical clustering/aspect model, a translation model adapted from statistical machine translation (Brown et al.), and a multi-modal extension to mixture of latent Dirichlet allocation

Learning to detect natural image boundaries using local brightness, color, and texture cues

by David R. Martin, Charless C. Fowlkes, Jitendra Malik - PAMI , 2004
"... The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these fe ..."
Abstract - Cited by 625 (18 self) - Add to MetaCart
The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.

Image retrieval: ideas, influences, and trends of the new age

by Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang - ACM COMPUTING SURVEYS , 2008
"... We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger ass ..."
Abstract - Cited by 485 (13 self) - Add to MetaCart
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
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... [Wang et al. 2001], a Bayesian framework based segmentation involving the Markov chain Monte Carlo technique [Tu and Zhu 2002], and an EM algorithm based segmentation using a Gaussian mixture model [=-=Carson et al. 2002-=-], forming blobs suitable for image querying and retrieval. A sequential segmentation approach that starts with texture features and refines segmentation using color features is explored in [Chen et a...

80 million tiny images: a large dataset for non-parametric object and scene recognition

by Antonio Torralba , Rob Fergus, William T. freeman - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
"... ..."
Abstract - Cited by 421 (18 self) - Add to MetaCart
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...age retrieval (CBIR) community. Their emphasis on really large data sets means that the chosen image representation is often relatively simple, e.g., color [17], wavelets [42], or crude segmentations =-=[9]-=-. This enables very fast retrieval of images similar to the query, for example, the Cortina system [33] demonstrates real-time retrieval from a 10 million image collection, using a combination of text...

Learning the Semantics of Words and Pictures

by Kobus Barnard, David Forsyth , 2000
"... We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on ..."
Abstract - Cited by 274 (12 self) - Add to MetaCart
We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and image features, it can be used for novel applications such as associating words with pictures, and unsupervised learning for object recognition. 1.
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...5). A number of other researchers have introduced systems for searching image databases. This work includes search by text [1, 2], search by image feature similarity [3-6], search by segment features =-=[7]-=-, search for specific types of images using more compressive methods [8, 9], and search by image sketch [1]. A few systems combine text and image data. Search using a simple conjunction of keywords an...

Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search

by Antonio Torralba, Aude Oliva, Monica S. Castelhano, John M. Henderson - PSYCHOLOGICAL REVIEW , 2006
"... Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an or ..."
Abstract - Cited by 258 (17 self) - Add to MetaCart
Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scenecentered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.

Building the gist of a scene: the role of global image features in recognition

by Aude Oliva, Antonio Torralba , 2006
"... ..."
Abstract - Cited by 250 (10 self) - Add to MetaCart
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...he organization of color blobs in the image (under this representation a view of a landscape corresponds to a blue blob on the top, a green blob on the bottom and a brownish blob in the center. e.g., =-=Carson et al., 2002-=-; Lipson et al., 1997; Oliva & Schyns, 2000). Despite the simplicity of such a representation, it is remarkable to note the reliability of scene recognition achieved by human observers when shown a ve...

Image Categorization by Learning and Reasoning with Regions

by Yixin Chen, James Z Wang, Donald Geman - Journal of Machine Learning Research , 2004
"... Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image cat ..."
Abstract - Cited by 195 (11 self) - Add to MetaCart
Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative.

Small codes and large image databases for recognition

by Antonio Torralba, Rob Fergus, Yair Weiss
"... The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In ..."
Abstract - Cited by 185 (7 self) - Add to MetaCart
The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it
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... retrieval (CBIR) community, although the emphasis on really large datasets means that the chosen image representation is often relatively simple, e.g. color [6], wavelets [29] or crude segmentations =-=[4]-=-. The Cortina system [22] demonstrates real-time retrieval from a 10 million image collection, using a combination of texture and edge histogram features. See Datta et al. for a survey of such methods...

A survey of content-based image retrieval with high-level semantics

by Ying Liu , Dengsheng Zhang , Guojun Lu , Wei-ying Ma , 2007
"... In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attemp ..."
Abstract - Cited by 150 (5 self) - Add to MetaCart
In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
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