@article {15067,
	title = {Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {34},
	year = {2012},
	month = {2012/03//},
	pages = {533 - 547},
	abstract = {A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.},
	keywords = {action prototype, actor location, brute-force computation, CMU action data set, distance measures, dynamic backgrounds, dynamic prototype sequence matching, flexible action matching, frame-to-frame distances, frame-to-prototype correspondence, hierarchical k-means clustering, human action recognition, Image matching, image recognition, Image sequences, joint probability model, joint shape, KTH action data set, large gesture data set, learning, learning (artificial intelligence), look-up table indexing, motion space, moving cameras, pattern clustering, prototype-to-prototype distances, shape-motion prototype-based approach, table lookup, training sequence, UCF sports data set, Video sequences, video signal processing, Weizmann action data set},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.147},
	author = {Zhuolin Jiang and Zhe Lin and Davis, Larry S.}
}
@conference {14227,
	title = {Learning shift-invariant sparse representation of actions},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {2630 - 2637},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or {\textquotedblleft}activated{\textquotedblright}) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L1 minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching Pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).},
	keywords = {action characterization, Action recognition, action retrieval, action synthesis, Character recognition, data compression, human motion capture, HUMANS, Image matching, Image motion analysis, image representation, Image sequences, Information retrieval, joint movements, large convex minimizations, learning (artificial intelligence), learning shift-invariant sparse representation, Matching pursuit algorithms, minimisation, Minimization methods, MoCap data compression, Motion analysis, motion capture analysis, motion disorder disease, motion sequences, orthogonal matching pursuit, Parkinson diagnosis, Parkinson{\textquoteright}s disease, Pursuit algorithms, shift-invariant basis functions, short basis functions, snippets, sparse linear combination, split Bregman algorithm, time series, time series data, Unsupervised learning, unsupervised learning algorithm},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539977},
	author = {Li,Yi and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Hui Ji}
}
@conference {15585,
	title = {Enhancements in robust feature matching},
	booktitle = {Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on},
	volume = {1},
	year = {2008},
	month = {2008/05//},
	pages = {505 - 508},
	abstract = {We present in this paper a number of new enhancements to a branch-and-bound algorithm given by Mount, Netanyahu, and Le Moigne [8] for feature matching. We introduce a new distance measure, which is more robust to the presence of outliers than the previously used measure, as well as a new variant of the search algorithm and a new search strategy. We also present experimental results, which show that these enhancements offer significant tangible improvements in performance.},
	keywords = {branch-and-bound algorithm, distance measure, Image Enhancement, Image matching, robust feature matching, tree searching},
	doi = {10.1109/ECTICON.2008.4600481},
	author = {Ratanasanya,S. and Mount, Dave and Netanyahu,N. S and Achalakul,T.}
}
@conference {18327,
	title = {Detection, analysis and matching of hair},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {741 - 748 Vol. 1 - 741 - 748 Vol. 1},
	abstract = {We develop computational models for measuring hair appearance for comparing different people. The models and methods developed have applications to person recognition and face image indexing. An automatic hair detection algorithm is described and results reported. A multidimensional representation of hair appearance is presented and computational algorithms are described. Results on a dataset of 524 subjects are reported. Identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair based identification.},
	keywords = {automatic hair detection, eigenface-based recognition, eigenface-hair based identification, Eigenvalues and eigenfunctions, face image indexing, face recognition, Feature extraction, hair analysis, hair appearance, hair attributes, hair matching, Image matching, image representation, multidimensional representation, person recognition},
	doi = {10.1109/ICCV.2005.75},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {15591,
	title = {Geo-registration of Landsat data by robust matching of wavelet features},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	volume = {4},
	year = {2000},
	month = {2000///},
	pages = {1610 -1612 vol.4 - 1610 -1612 vol.4},
	abstract = {The goal of our project is to build an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. Integrated within an automated mass processing/analysis system for Landsat data (REALM), the input to our registration method consists of scenes that have been geometrically and radiometrically corrected, as well as pre-processed for the detection of clouds and cloud shadows. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. This paper describes our registration process, including the use of a database of landmark chips, a feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes of the four corners of the scene, a subset of chips which represent landmarks included in the scene are extracted from the database. For each of these selected landmark chips, a corresponding window is extracted from the scene, and each chip-window pair is registered using our robust wavelet feature matching. First results and future directions are presented in the paper},
	keywords = {atmospheric techniques, automated mass processing/analysis system, chip-window pair, cloud shadows, Clouds, Feature extraction, feature matching, geo-registration, geometrically corrected scene, geophysical signal processing, Image matching, image registration, landmark chips, Landsat chips, Landsat data, Landsat-5 data, Landsat-7 data, overcomplete wavelet representation, pre-processed scenes, radiometrically corrected scene, REALM, Remote sensing, robust matching, robust wavelet feature matching, scenes, statistically robust techniques, sub-pixel accuracy registration, wavelet features, Wavelet transforms, window},
	doi = {10.1109/IGARSS.2000.857287},
	author = {Le Moigne,J. and Netanyahu,N. S and Masek,J. G and Mount, Dave and Goward,S. and Honzak,M.}
}
@conference {18454,
	title = {Pictorial query trees for query specification in image databases},
	booktitle = {Fourteenth International Conference on Pattern Recognition, 1998. Proceedings},
	volume = {1},
	year = {1998},
	month = {1998/08//},
	pages = {919-921 vol.1 - 919-921 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A technique that enables specifying complex queries in image databases using pictorial query trees is presented. The leaves of a pictorial query tree correspond to individual pictorial queries that specify which objects should appear in the target images as well as how many occurrences of each object are required. In addition, the minimum required certainty of matching between query-image objects and database-image objects, as well as spatial constraints that specify bounds on the distance between objects and the relative direction between them are also specified. Internal nodes in the query tree represent logical operations (AND, OR, XOR) and their negations on the set of pictorial queries (or subtrees) represented by its children. The syntax of query trees is described. Algorithms for processing individual pictorial queries and for parsing and computing the overall result of a pictorial query tree are outlined},
	keywords = {Automation, complex queries, Computer science, content-based retrieval, Database systems, database-image objects, Educational institutions, Electrical capacitance tomography, grammars, Image databases, Image matching, parsing, pictorial query trees, Postal services, query specification, query-image objects, spatial constraints, syntax, visual databases},
	isbn = {0-8186-8512-3},
	doi = {10.1109/ICPR.1998.711383},
	author = {Soffer,A. and Samet, Hanan and Zotkin,Dmitry N}
}
