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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
, 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

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

by Michal Aharon, et al. , 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
Abstract - Cited by 935 (41 self) - Add to MetaCart
by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse

CLASSIC: consistent longitudinal alignment and segmentation for serial image computing

by Zhong Xue, Dinggang Shen, Christos Davatzikos - NeuroImage , 2006
"... Abstract. This paper proposes a temporally-consistent and spatiallyadaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorpora ..."
Abstract - Cited by 38 (15 self) - Add to MetaCart
incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging or disease. Morphological changes, such as growth or atrophy, are also estimated

Creating efficient codebooks for visual recognition

by Frederic Jurie, Bill Triggs - In Proceedings of the IEEE International Conference on Computer Vision , 2005
"... Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. Codebooks are usually constructed by using a method such as k-means to cluster the descriptor vect ..."
Abstract - Cited by 276 (22 self) - Add to MetaCart
Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. Codebooks are usually constructed by using a method such as k-means to cluster the descriptor

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

by Radhakrishna Achanta, Kevin Smith, Aurelien Lucchi, Pascal Fua - PAMI
"... Abstract—Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superp ..."
Abstract - Cited by 222 (3 self) - Add to MetaCart
-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate

An Adaptive Crossover-Imaged Clustering Algorithm

by Nancy P. Lin, Chung-i Chang, Hao-en Chueh, Hung-jen Chen, Wei-hua Hao
"... Abstract:- The grid-based clustering algorithm is an efficient clustering algorithm, but its effect is seriously influenced by the size of the predefined grids and the threshold of the significant cells. The data space will be partitioned into a finite number of cells to form a grid structure and th ..."
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and then performs all clustering operations on this obtained grid structure. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, an Adaptive Crossover-Imaged Clustering Algorithm, called ACICA, is proposed

An Adaptive Clustering Algorithm for Image Segmentation

by unknown authors
"... Abstract-The problem of segmenting images of objects with smooth surfaces is considered. The algorithm we present is a generalization of the,K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints a re included by th ..."
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Abstract-The problem of segmenting images of objects with smooth surfaces is considered. The algorithm we present is a generalization of the,K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints a re included

An adaptive spatial fuzzy clustering algorithm for 3-DMR image segmentation

by Alan Wee-chung Liew, Hong Yan, Senior Member - IEEE Trans. on Medical Imaging , 2003
"... Abstract—An adaptive spatial fuzzy c-means clustering algo-rithm is presented in this paper for the segmentation of three-di-mensional (3-D) magnetic resonance (MR) images. The input im-ages may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into accou ..."
Abstract - Cited by 45 (6 self) - Add to MetaCart
Abstract—An adaptive spatial fuzzy c-means clustering algo-rithm is presented in this paper for the segmentation of three-di-mensional (3-D) magnetic resonance (MR) images. The input im-ages may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes

QCluster: Relevance Feedback Using Adaptive Clustering for Content Based Image Retrieval

by Deok-hwan Kim, Chin-wan Chung - Proc. ACM SIGMOD , 2003
"... The learning-enhanced relevance feedback has been one of the most active research areas in content-based image re-trieval in recent years. However, few methods using the rel-evance feedback are currently available to process relatively complex queries on large image databases. In the case of complex ..."
Abstract - Cited by 42 (0 self) - Add to MetaCart
-tive queries in the feature space. In this paper, we propose a new content-based image retrieval method using adaptive classification and cluster-merging to find multiple clusters of a complex image query. When the measures of a retrieval method are invariant under linear transformations, the method can

Image Segmentation Based on Adaptive Cluster Prototype Estimation

by Alan Wee , Member, IEEE Chung Liew , Senior Member, IEEE Hong Yan , Member, IEEE N F Law
"... Abstract-An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration t ..."
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Abstract-An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration
Results 1 - 10 of 880