This paper introduces algorithms for determining the “relevance” of each frame (and objects within the frame) to the scenes present in a given video sequence. Specifically, we introduce a rank number which determines the importance of each frame in contributing to the scenes in a video sequence. The rank number is computed based on the temporal duration of motion-tracked objects and pixels in v...

Segmentation is an important stage in image/video analysis and understanding. There are many different approaches and algorithms for image/video segmentation, hence their evaluation is also important in order to assess the quality of segmentation results. Nonetheless, so far there was little research aimed specifically at evaluation of video segmentation quality. In this article, we propose the...

We propose a video representation, the Video Scene Trajectory, computed from localised temporal-change in order to capture the holistic action content in a sequence. Such a representation is critical for action based genres such as surveillance. We show that an analysis of the trajectory shape is able to produce a temporal segmentation. Furthermore, we build an action based video summary using ...

Though being a vital part of video indexing and retrieval systems, key-frame extraction algorithms have been based mainly on the analysis of various frame similarities and their later clustering. This work broadens the spectra of the analysis by focusing on the spatio-temporal region relations present in the scene to determine the most representative frame in the shot. It applies efficient vide...

This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatio-temporal constraints. Given consecutive frames, an optimiz...

Object-based video data representations enable unprecedented functionalities of content access and manipulation. In this paper, we present an integrated approach using region-based analysis for semantic video object segmentation and retrieval. We first present an active system that combines low-level region segmentation with user inputs for defining and tracking semantic video objects. The prop...

In the framework of computer vision, the spatio-temporal se-gmentation procedure plays a central role. It aims at identifying in the input image, semantically meaningful features that are relevant for the problem at hand. In this paper, these features are selected to be the objects forming the scene. The objects are deened by their properties of temporal and spatial coherence through the video ...

Video classi cation and segmentation are fundamental steps for e cient accessing, retrieving and browsing large amount of video data. We have developed a scene classi cation scheme using a Hidden MarkovModel (HMM)based classi er. By utilizing the temporal behaviors of di erent scene classes, HMM classi er can e ectively classify video segments into one of the prede ned scene classes. In this pa...

This paper presents an object contour detection using the object shape model. First, we detect the change of background luminance ratio outside the bounding box of object. Second, the temporal segmentation is obtained by mesh based motion activity detection. A new combined mask is composed of temporal segmentation result and shape model by OR operation. Finally, for modification of the mask, we...

There is a growing need for new representations of video that allow not only compact storage of data but also content-based functionalities such as search and manipulation of objects. We present here a prototype system, called NeTra-V, that is currently being developed to address some of these content related issues. The system has a twostage video processing structure: a global feature extract...