@article {12542,
	title = {Machine Recognition of Human Activities: A Survey},
	journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
	volume = {18},
	year = {2008},
	month = {2008/11//},
	pages = {1473 - 1488},
	abstract = {The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.},
	keywords = {activity;low-level, annotation;human, cameras;video, content-based, factors;image, processing;machine, processing;rate-invariant, recognition;image, recognition;midlevel, representation;image, representation;video, representations;semantic, retrieval;human, sequences;, summarization;content-based, video},
	isbn = {1051-8215},
	doi = {10.1109/TCSVT.2008.2005594},
	author = {Turaga,P. and Chellapa, Rama and V.S. Subrahmanian and Udrea,O.}
}
@conference {16845,
	title = {Indexing Methods for Similarity Searching},
	booktitle = {Current Trends in Computer Science, 2007. ENC 2007. Eighth Mexican International Conference on},
	year = {2007},
	month = {2007/09//},
	pages = {xv - xv},
	abstract = {An overview is given of the various techniques and issues involved in providing indexing support for similarity searching. Similarity searching is a crucial part of retrieval in multimedia databases used for applications such as pattern recognition, image databases, and content-based retrieval. It involves finding objects in a data set S that are similar to a query object q based on some distance measure d which is usually a distance metric. The search process is usually achieved by means of nearest neighbor finding. Existing methods for handling similarity search in this setting fall into one of two classes. The first is based on mapping to a vector space. The vector space is usually of high dimension which requires special handling due to the fact indexing methods do not discriminate well in such spaces. In particular, the query regions often overlap all of the blocks that result from the decomposition of the underlying space. This has led to some special solutions that make use of a sequential scan. An alternative is to use dimension reduction to find a mapping from a high-dimensional space into a low-dimensional space by finding the most discriminating dimensions and then index the data using one of a number of different data structures such as k-d trees, R-trees, quadtrees, etc. The second directly indexes the objects based on distances making use of data structures such as the vp-tree, M-tree, etc.},
	keywords = {content-based, database;indexing, database;pattern, databases;, indexing;multimedia, methods;multimedia, recognition;similarity, retrieval;data, searching;data, structures;database, structures;image},
	doi = {10.1109/ENC.2007.9},
	author = {Samet, Hanan}
}
@article {18194,
	title = {Data hiding in image and video .II. Designs and applications},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/06//},
	pages = {696 - 705},
	abstract = {For pt. I see ibid., vol.12, no.6, p.685-95 (2003). This paper applies the solutions to the fundamental issues addressed in Part I to specific design problems of embedding data in image and video. We apply multilevel embedding to allow the amount of embedded information that can be reliably extracted to be adaptive with respect to the actual noise conditions. When extending the multilevel embedding to video, we propose strategies for handling uneven embedding capacity from region to region within a frame as well as from frame to frame. We also embed control information to facilitate the accurate extraction of the user data payload and to combat such distortions as frame jitter. The proposed algorithm can be used for a variety of applications such as copy control, access control, robust annotation, and content-based authentication.},
	keywords = {access, annotation;, authentication;, capacity;, conditions;, content-based, CONTROL, control;, copy, data, distortions;, EMBEDDING, embedding;, encapsulation;, extraction;, frame, hiding;, image, information;, jitter;, message, multilevel, NOISE, noise;, payload, processing;, robust, signal, uneven, user, video},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.810589},
	author = {M. Wu and Yu,H. and Liu,Bede}
}
@conference {16914,
	title = {Content-based image retrieval using Fourier descriptors on a logo database},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {521 - 524 vol.3 - 521 - 524 vol.3},
	abstract = {A system that enables the pictorial specification of queries in an image database is described. The queries are comprised of rectangle, polygon, ellipse, and B-spline shapes. The queries specify which shapes should appear in the target image as well as spatial constraints on the distance between them and their relative position. The retrieval process makes use of an abstraction of the contour of the shape which is invariant against translation, scale, rotation, and starting point, that is based on the use of Fourier descriptors. These abstractions are used in a system to locate logos in an image database. The utility of this approach is illustrated using some sample queries.},
	keywords = {abstraction;, analysis;, constraints;, content-based, contour, database, database;, databases;, descriptors;, detection;, edge, Fourier, image, logos;, pictorial, processing;, query, retrieval;, SHAPE, spatial, specification;, theory;, visual},
	doi = {10.1109/ICPR.2002.1047991},
	author = {Folkers,A. and Samet, Hanan}
}
