Results 1 - 10 of 222

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 657 (39 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

Support vector machines for multiple-instance learning

by Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann - Advances in Neural Information Processing Systems 15 , 2003
"... This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the ..."
Abstract - Cited by 309 (2 self) - Add to MetaCart
This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharmaceutical data set and on applications in automated image indexing and document categorization. 1
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A framework for multiple-instance learning

by Oded Maron - In Advances in Neural Information Processing Systems , 1998
"... Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled ..."
Abstract - Cited by 259 (2 self) - Add to MetaCart
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem. 1

Supervised learning of semantic classes for image annotation and retrieval

by Gustavo Carneiro, Antoni B. Chan, Pedro J. Moreno, Nuno Vasconcelos - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007
"... Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to- ..."
Abstract - Cited by 221 (18 self) - Add to MetaCart
Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning. Index Terms—Content-based image retrieval, semantic image annotation and retrieval, weakly supervised learning, multiple instance learning, Gaussian mixtures, expectation-maximization, image segmentation, object recognition. 1
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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 191 (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.

EM-DD: An Improved Multiple-Instance Learning Technique

by Qi Zhang, Sally A. Goldman - In Advances in Neural Information Processing Systems , 2001
"... We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. ..."
Abstract - Cited by 160 (5 self) - Add to MetaCart
We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm.
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Automatic Multimedia Cross-modal Correlation Discovery

by Jia-yu Pan, Hyung-jeong Yang, Christos Faloutsos, Pinar Duygulu , 2004
"... Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph- ..."
Abstract - Cited by 138 (16 self) - Add to MetaCart
Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph-based approach, "MMG", to discover such cross-modal correlations. Our

Solving the multiple-instance problem: A lazy learning approach

by Jun Wang - In Proc. 17th International Conf. on Machine Learning , 2000
"... As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or negative. Current research mainly concentrates on adapting traditional concept learning to solve thi ..."
Abstract - Cited by 125 (5 self) - Add to MetaCart
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or negative. Current research mainly concentrates on adapting traditional concept learning to solve this problem. In this paper we investigate the use of lazy learning and Hausdorff distance to approach the multipleinstance problem. We present two variants of the K-nearest neighbor algorithm, called Bayesian-KNN and Citation-KNN, solving the multipleinstance problem. Experiments on the Drug discovery benchmark data show that both algorithms are competitive with the best ones conceived in the concept learning framework. Further work includes exploring of a combination of lazy and eager multiple-instance problem classifiers. 1.

Multi-instance multilabel learning with application to scene classification

by Zhi-hua Zhou, Min-ling Zhang - In Advances in Neural Information Processing Systems 19 , 2007
"... In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e.g. an image usually contains multiple patches each of which can be describe ..."
Abstract - Cited by 108 (26 self) - Add to MetaCart
In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e.g. an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways. We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multiinstance learning and multi-label learning. Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification. 1
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MILES: multiple-instance learning via embedded instance selection

by Yixin Chen, Jinbo Bi, James Z. Wang, Senior Member - IEEE Trans Pattern Anal Mach Intell
"... Abstract—Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption th ..."
Abstract - Cited by 100 (3 self) - Add to MetaCart
Abstract—Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty. Index Terms—Multiple-instance learning, feature subset selection, 1-norm support vector machine, image categorization, object recognition, drug activity prediction. Ç 1
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