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Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, Twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets...
Mining multivariate time series data by clustering is an important research topic. Time series can be clustered by standard approaches like k-means, or by advanced methods such as subspace clustering and triclustering. A problem with these new methods is the lack of a general evaluation scheme that can be used by researchers to understand and compare the algorithms, publications on new algorithms...
When comparing clustering results, any evaluation metric breaks down the available information to a single number. However, a lot of evaluation metrics are around, that are not always concordant nor easily interpretable in judging the agreement of a pair of clusterings. Here, we provide a tool to visually support the assessment of clustering results in comparing multiple clusterings. Along the way,...
The quality of codebook is the determinant factor in BoW-based copy detection strategies. However, most of the adopted codebook construction algorithms are derived from image retrieval or object recognition, which neglect the robustness in partitioning original features and copy features (especially those with serious transformations) into the same group. To deal with this problem, we have developed...
Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass...
In this paper, we investigate the parameters underpinning our previously presented system for detecting unusual events in surveillance applications [1]. The system identifies anomalous events using an unsupervised data-driven approach. During a training period, typical activities within a surveilled environment are modeled using multi-modal sensor readings. Significant deviations from the established...
Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like algorithm selection and parameterization...
Determining the number of clusters present in a data set automatically is a very important problem. Conventional clustering techniques assume a certain number of clusters, and then try to find out the possible cluster structure associated to the above number. For very large and complex data sets it is not easy to guess this number of clusters. There exists validity based clustering techniques, which...
In this paper, the advantages of ensemble methods are adapted to image categorization. A novel method is introduced for image categorization by constructing vocabulary ensembles using different clustering algorithms in the popular vocabulary approach. The vocabulary approach describes an image as a bag of discrete visual words, where the frequency distributions of these words are used for image categorization...
Video summarization is a useful tool which allows a user to grasp rapidly the essence of a video. In the development of this research topic we propose a new method based on different individual content segmentation and selection tools in a collaborative system. The main innovation of this work is to merge results from different approaches, so as to benefit from their respective qualities. Our system...
We describe an approach to identifying specific settings in large collections of photographs corresponding to a visual diary. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable...
It's necessary to discuss the topology model of digital images for integrating remote sensing and geographic information system in higher levels. Based on cellular complex theories, a hierarchical image representation is presented that maintains both raster and vector representations of an object inside a same data structure and to translate between spatial concepts in image understanding and mining...
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