Video Object Relevance Metrics for Overall Segmentation Quality Evaluation
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Video Object Relevance Metrics for Overall Segmentation Quality Evaluation Paulo Correia and Fernando Pereira Instituto Superior T´ecnico – Instituto de Telecomunicac¸o˜es, Av. Rovisco Pais, 1049-001 Lisboa, Portugal Received 28 February 2005; Revised 31 May 2005; Accepted 31 July 2005 Video object segmentation is a task that humans perform efficiently and effectively, but which is difficult for a computer to perform. Since video segmentation plays an important role for many emerging applications, as those enabled by the MPEG-4 and MPEG-7 standards, the ability to assess the segmentation quality in view of the application targets is a relevant task for which a standard, or even a consensual, solution is not available. This paper considers the evaluation of overall segmentation partitions quality, highlighting one of its major components: the contextual relevance of the segmented objects. Video object relevance metrics are presented taking into account the behaviour of the human visual system and the visual attention mechanisms. In particular, contextual relevance evaluation takes into account the context where an object is found, exploiting, for instance, the contrast to neighbours or the position in the image. Most of the relevance metrics proposed in this paper can also be used in contexts other than segmentation quality evaluation, such as object-based rate control algorithms, description creation, or image and video quality evaluation. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
When working with image and video segmentation, the major objective is to design an algorithm that produces appropriate segmentation results for the particular goals of the application addressed. Nowadays, several applications exploit the representation of a video scene as a composition of video objects, taking advantage of the object-based standards for coding and representation specified by ISO: MPEG-4 [1] and MPEG-7 [2]. Examples are interactive applications that associate specific information and interactive “hooks” to the objects present in a given video scene, or applications that select different coding strategies, in terms of both techniques and parameter configurations, to encode the various video objects in the scene. To enable such applications, the assessment of the image and video segmentation quality in view of the application goals assumes a crucial importance. In some cases, segmentation is automatically obtained using techniques like chromakeying at the video production stage, but often the segmentation needs to be computed based on the image and video contents by using appropriate segmentation algorithms. Segmentation quality evaluation allows assessing the segmentation algorithm’s adequacy for the targeted application, and it provides information that can be used to optimise the segmentation algorithm’s behaviour by using the so-called relevance feedback mechanism [3].
Currently, there are no standard, or commonly accepted, methodologies available for objective evaluation of image
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