K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD

This paper describes an approach to identify individuals with suspicious objects in a crowd. It is based on a well-known image retrieval problem as applied to mobile visual search. In many cases, the process of building a hierarchical tree uses k-means clustering followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is randomly generated. This may lead to a congested clustering which can cause problems in grouping large real-time data. To overcome this problem we have applied the Indian Buffet stochastic process approach in this paper to the clustering problem. We present examples illustrating our metho

Motion characterization from temporal cooccurrences of local motion-based measures for video indexing

This paper describes an original approach for motion interpretation with a view to content-based video indexing. We exploit a statistical analysis of the temporal distribution of appropriate local motion-based measures to perform a global motion characterization. We consider motion features extracted from temporal cooccurrence matrices, and related to properties of homogeneity, acceleration or complexity. Results on various real video sequences are reported and provide a first validation of the approach. 1

Scenario-Based Query Processing for Video-Surveillance Archives

Cataloged from PDF version of article.Automated video surveillance has emerged as a trendy application domain in recent years, and accessing the semantic content of surveillance video has become a challenging research area. The results of a considerable amount of research dealing with automated access to video surveillance have appeared in the literature; however, significant semantic gaps in event models and content-based access to surveillance video remain. In this paper, we propose a scenario-based query-processing system for video surveillance archives. In our system, a scenario is specified as a sequence of event predicates that can be enriched with object-based low-level features and directional predicates. We introduce an inverted tracking scheme, which effectively tracks the moving objects and enables view-based addressing of the scene. Our query-processing system also supports inverse querying and view-based querying, for after-the-fact activity analysis. We propose a specific surveillance query language to express the supported query types in a scenario-based manner. We also present a visual query-specification interface devised to facilitate the query-specification process. We have conducted performance experiments to show that our query-processing technique has a high expressive power and satisfactory retrieval accuracy in video surveillance. (C) 2009 Elsevier Ltd. All rights reserved

Discrete language models for video retrieval

Finding relevant video content is important for producers of television news, documentanes and commercials. As digital video collections become more widely available, content-based video retrieval tools will likely grow in importance for an even wider group of users. In this thesis we investigate language modelling approaches, that have been the focus of recent attention within the text information retrieval community, for the video search task. Language models are smoothed discrete generative probability distributions generally of text and provide a neat information retrieval formalism that we believe is equally applicable to traditional visual features as to text. We propose to model colour, edge and texture histogrambased features directly with discrete language models and this approach is compatible with further traditional visual feature representations. We provide a comprehensive and robust empirical study of smoothing methods, hierarchical semantic and physical structures, and fusion methods for this language modelling approach to video retrieval. The advantage of our approach is that it provides a consistent, effective and relatively efficient model for video retrieval

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing