Abstract

Navigation through large multimedia collections that include videos and images still remains cumbersome. In this paper, we introduce a novel method to visualize and navigate through the collection by creating a mosaic image that visually represents the compilation. This image is generated by a labeling-based layout algorithm using various sizes of sample tile images from the collection. Each tile represents both the photographs and video files representing scenes selected by matching algorithms. This generated mosaic image provides a new way for thematic video and visually summarizes the videos. Users can generate these mosaics with some predefined themes and layouts, or base it on the results of their queries. Our approach supports automatic generation of these layouts by using meta-information such as color, time-line and existence of faces or manually generated annotated information from existing systems (e.g., the Family Video Archive).

Interactive Video Mosaic

]]> http://prof.irfanessa.com/2006/10/22/paper-in-acm-multimedia-2006-interactive-mosaic-generation-for-video-navigation/feed/ 0 http://prof.irfanessa.com/2006/06/14/ieeexplore-learning-temporal-sequence-model-from-partially-labeled-data/ http://prof.irfanessa.com/2006/06/14/ieeexplore-learning-temporal-sequence-model-from-partially-labeled-data/#comments Wed, 14 Jun 2006 17:06:45 +0000 Irfan Essa http://academics.irfanessa.com/2006/06/14/ieeexplore-learning-temporal-sequence-model-from-partially-labeled-data/ Yifan Shi, Bobick, A. Essa, I. (2006), “Learning Temporal Sequence Model from Partially Labeled Data” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006
Volume: 2, page(s): 1631 – 1638, ISSN: 1063-6919, ISBN: 0-7695-2597-0, Digital Object Identifier: 10.1109/CVPR.2006.174 [IEEEXplore]

Abstract

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure – the nodes – are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types – vision and inertial measurements – in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.

Abstract

Blind source separation algorithms typically require that the number of sources are known in advance. However, it is often the case that the number of sources change over time and that the total number is not known. Existing source separation techniques require source number estimation methods to determine how many sources are active within the mixture signals. These methods typically operate on the covariance matrix of mixture recordings and require fewer active sources than mixtures. When sources do not overlap in the time-frequency domain, more sources than mixtures may be detected and then separated. However, separating more sources than mixtures when sources overlap in time and frequency poses a particularly difficult problem. This paper addresses the issue of source detection when more sources than sensors overlap in time and frequency. We show that repetitive structure in the form of time-time correlation matrices can reveal when each source is active

The Digital Video Special Effects class of Spring 2006 (CS 4480) PREMIERED their final productions on April 28, 2006 in TSRB Auditorium at 5:15pm. Students showed and discussed the short video segments (less than 1 minutes each) that they produced to showcase the technical special effects they have generated during the course of the semester.