@conference {12471,
	title = {Automatic target recognition based on simultaneous sparse representation},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1377 - 1380},
	abstract = {In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations.},
	keywords = {(artificial, algorithm;feature, based, classification;iterative, classification;learning, Comanche, data, dictionary;matching, extraction;image, forward-looking, infrared, intelligence);military, learning, MATCHING, matrix;dictionary, measure;military, methods;learning, orthogonal, pursuit, pursuit;confusion, recognition;class, recognition;target, representation;feature, representation;sparse, set;automatic, signal, similarity, simultaneous, sparse, supervised, systems;object, target, target;simultaneous, tracking;},
	doi = {10.1109/ICIP.2010.5652306},
	author = {Patel, Vishal M. and Nasrabadi,N.M. and Chellapa, Rama}
}
@conference {16818,
	title = {Images in News},
	booktitle = {Pattern Recognition (ICPR), 2010 20th International Conference on},
	year = {2010},
	month = {2010/08//},
	pages = {3240 - 3243},
	abstract = {A system, called News Stand, is introduced that automatically extracts images from news articles. The system takes RSS feeds of news article and applies an online clustering algorithm so that articles belonging to the same news topic can be associated with the same cluster. Using the feature vector associated with the cluster, the images from news articles that form the cluster are extracted. First, the caption text associated with each of the images embedded in the news article is determined. This is done by analyzing the structure of the news article{\textquoteright}s HTML page. If the caption and feature vector of the cluster are found to contain keywords in common, then the image is added to an image repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach. This repository also contained more than 86.8 million keywords associated with the images. The key contribution of this work is that it combines clustering and natural language processing tasks to automatically create a large corpus of news images with good quality tags or meta-information so that interesting vision tasks can be performed on it.},
	keywords = {algorithm;feature, articles;news, clustering, clustering;publishing;, extraction;hypermedia, extraction;image, feeds;caption, HTML, images;news, LANGUAGE, languages;image, markup, page;RSS, processing;news, processing;pattern, repository;meta-information;natural, retrieval;natural, stand;online, text;feature, vector;image},
	doi = {10.1109/ICPR.2010.792},
	author = {Sankaranarayanan,J. and Samet, Hanan}
}
@conference {13137,
	title = {Robust Object Tracking with Regional Affine Invariant Features},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.},
	keywords = {affine, algorithm;feature, analysis;image, analysis;pixel, consistency;regional, detection;motion, detection;tracking;, extraction;image, feature, features;robust, invariant, matching;image, MOTION, object, resolution;object, tracking},
	doi = {10.1109/ICCV.2007.4408948},
	author = {Tran,Son and Davis, Larry S.}
}
