@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 {12450,
	title = {Entropy rate superpixel segmentation},
	booktitle = {2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2011},
	month = {2011/06/20/25},
	pages = {2097 - 2104},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. We present a novel graph construction for images and show that this construction induces a matroid - a combinatorial structure that generalizes the concept of linear independence in vector spaces. The segmentation is then given by the graph topology that maximizes the objective function under the matroid constraint. By exploiting submodular and mono-tonic properties of the objective function, we develop an efficient greedy algorithm. Furthermore, we prove an approximation bound of {\textonehalf} for the optimality of the solution. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.},
	keywords = {balancing function, Berkeley segmentation benchmark, Complexity theory, Entropy, entropy rate, graph construction, graph theory, graph topology, greedy algorithm, Greedy algorithms, homogeneous clusters, Image edge detection, Image segmentation, matrix algebra, matroid constraint, measurement, pattern clustering, Random variables, standard evaluation metrics, superpixel segmentation, vector spaces},
	isbn = {978-1-4577-0394-2},
	doi = {10.1109/CVPR.2011.5995323},
	author = {Ming-Yu Liu and Tuzel, O. and Ramalingam, S. and Chellapa, Rama}
}
@conference {17181,
	title = {Group-in-a-Box Layout for Multi-faceted Analysis of Communities},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {354 - 361},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.},
	keywords = {Algorithm design and analysis, category based social graph partitions, clustered graphs, clustering, Clustering algorithms, Communities, data visualisation, force-directed, gender, geographic location, graph layout algorithms, graph theory, group-in-a-box, group-in-a-box layout, Image edge detection, Layout, meta-layout, multifaceted community analysis, network subgraph visualization, network visualization, pattern clustering, profession, semantic substrates, Social network services, social networking (online), social networks, treemap space filling technique, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.139},
	author = {Rodrigues,E.M. and Milic-Frayling,N. and Smith,M. and Shneiderman, Ben and Hansen,D.}
}
@conference {15064,
	title = {An improved mean shift tracking method based on nonparametric clustering and adaptive bandwidth},
	booktitle = {Machine Learning and Cybernetics, 2008 International Conference on},
	volume = {5},
	year = {2008},
	month = {2008/07//},
	pages = {2779 - 2784},
	abstract = {An improved mean shift method for object tracking based on nonparametric clustering and adaptive bandwidth is presented in this paper. Based on partitioning the color space of a tracked object by using a modified nonparametric clustering, an appearance model of the tracked object is built. It captures both the color information and spatial layout of the tracked object. The similarity measure between the target model and the target candidate is derived from the Bhattacharyya coefficient. The kernel bandwidth parameters are automatically selected by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate using the bandwidth matrix and the modified weight function. The experimental results show that the method can converge in an average of 2.6 iterations per frame.},
	keywords = {adaptive bandwidth, appearance model, bandwidth matrix, Bhattacharyya coefficient, color information, color space partitioning, image colour analysis, iterative procedure, kernel bandwidth parameter, kernel density estimate, log-likelihood function, mean shift tracking method, modified weight function, nonparametric clustering, Object detection, object representation, object tracking, pattern clustering, similarity measure, spatial layout, target candidate, target model, tracking},
	doi = {10.1109/ICMLC.2008.4620880},
	author = {Zhuolin Jiang and Li,Shao-Fa and Jia,Xi-Ping and Zhu,Hong-Li}
}
@conference {18649,
	title = {A Statistical Analysis of Attack Data to Separate Attacks},
	year = {2006},
	month = {2006/06//},
	pages = {383 - 392},
	abstract = {This paper analyzes malicious activity collected from a test-bed, consisting of two target computers dedicated solely to the purpose of being attacked, over a 109 day time period. We separated port scans, ICMP scans, and vulnerability scans from the malicious activity. In the remaining attack data, over 78\% (i.e., 3,677 attacks) targeted port 445, which was then statistically analyzed. The goal was to find the characteristics that most efficiently separate the attacks. First, we separated the attacks by analyzing their messages. Then we separated the attacks by clustering characteristics using the K-Means algorithm. The comparison between the analysis of the messages and the outcome of the K-Means algorithm showed that 1) the mean of the distributions of packets, bytes and message lengths over time are poor characteristics to separate attacks and 2) the number of bytes, the mean of the distribution of bytes and message lengths as a function of the number packets are the best characteristics for separating attacks},
	keywords = {attack data statistical analysis, attack separation, computer crime, Data analysis, data mining, ICMP scans, K-Means algorithm, pattern clustering, port scans, statistical analysis, vulnerability scans},
	doi = {10.1109/DSN.2006.9},
	author = {Michel Cukier and Berthier,R. and Panjwani,S. and Tan,S.}
}
@article {17259,
	title = {Interactively exploring hierarchical clustering results [gene identification]},
	journal = {Computer},
	volume = {35},
	year = {2002},
	month = {2002/07//},
	pages = {80 - 86},
	abstract = {To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts},
	keywords = {algorithmic methods, arrays, Bioinformatics, biological data sets, biology computing, Data analysis, data mining, data visualisation, Data visualization, DNA, Fluorescence, gene functions, gene identification, gene profiles, Genetics, Genomics, Hierarchical Clustering Explorer, hierarchical systems, interactive exploration, interactive information visualization tool, interactive systems, Large screen displays, meaningful cluster identification, metrics, microarray data analysis, pattern clustering, pattern extraction, Process control, Sensor arrays, sequenced genomes, Tiles},
	isbn = {0018-9162},
	doi = {10.1109/MC.2002.1016905},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
