Analysis of video sequences: table of content and index creation

This paper deals with the representation of video sequences useful for tasks such as long-term analysis, indexing or browsing. A Table Of Content and index creation algorithm is presented, as well as additional tools involved in their creation. The proposed method does not assume any a priori knowledge about the content or the structure of the video. It is therefore a generic technique. Some examples are presented in order to assess the performance of the algorithmPeer ReviewedPostprint (published version

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

A Short Survey on Data Clustering Algorithms

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end

Visually driven analysis of movement data by progressive clustering

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories

Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include