The problem of content characterization of sports videos is of
great interest because sports video appeals to large audiences and
its efficient distribution over various networks should contribute
to widespread usage of multimedia services. In this paper we
analyze several techniques proposed in literature for content
characterization of sports videos. We focus this analysis on the
typology of the signal (audio, video, text captions, ...) from
which the low-level features are extracted. First we consider the
techniques based on visual information, then the methods based on
audio information, and finally the algorithms based on
audio-visual cues, used in a multi-modal fashion. This analysis
shows that each type of signal carries some peculiar information,
and the multi-modal approach can fully exploit the multimedia
information associated to the sports video. Moreover, we observe
that the characterization is performed either considering what
happens in a specific time segment, observing therefore the
features in a "static" way, or trying to capture their "dynamic"
evolution in time. The effectiveness of each approach depends
mainly on the kind of sports it relates to, and the type of
highlights we are focusing on.
The segmentation of video sequences into regions underlying a coherent motion is one of the most important processing in video analysis and coding. In this paper, we propose a reliability measure that indicates to what extent an affine motion model represents the motion of an image region. This reliability measure is then proposed as a criterion to coherently merge moving image regions in a Minimum Description Length (MDL) framework. To overcome the region-based motion estimation and segmentation chicken and egg problem, the motion field estimation and the segmentation task are treated separately. After a global motion compensation, a local motion field estimation is carried out starting from a translational motion model. Concurrently, a Markov Random Field model based algorithm provides for an initial static image partition. The motion estimation and segmentation problem is then formulated in the view of the MDL principle. A merging stage based on a directed weighted graph gives the final spatio-temporal segmentation. The simulation results show the effectiveness of the proposed algorithm.
In this paper we describe an algorithm for image interpolation that takes into account the global motion of the camera. A description of the motion field in terms of pan and zoom parameters (global motion) and displacement of independent moving objects (local motion) is carried out. First the estimated global parameters are employed to compensate the images of the camera motion. Then the missing images are interpolated using the local motion information. As a last step the images are reconstructed in their correct dimension considering the global motion information. The performances of the proposed algorithm has been tested within a multilayer video coding scheme. The simulation results show the effectiveness of the proposed motion compensating interpolation algorithm that uses global-local description of the motion field.
The paper presents the main features of a prototype image retrieval system, nicknamed Imagine. In this system, the image database is located in a site remote from the user workstation. The key issues in developing the prototype have been the response time and scalability, or the ability of maintaining a set of basic functionalities in a wide range of workstation performances and network digital rates. The paper focuses on the problems related to the image visualization process in a workstation with a limited number of reproducible colors. Three different approaches, split, shared, and generic colormap, are presented and compared.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.