Semantic Video Analysis and Understanding

Semantic Video Analysis and Understanding

Vasileios Mezaris (Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece) and Georgios Th. Papadopoulos (Aristotle University of Thessaloniki, Greece and Informatics and Telematics Institute, Centre for Research and)
DOI: 10.4018/978-1-60566-026-4.ch543
OnDemand PDF Download:


Access to video content, either amateur or professional, is nowadays a key element in business environments, as well as everyday practice for individuals all over the world. The widespread availability of inexpensive video capturing devices, the significant proliferation of broadband Internet connections and the development of innovative video sharing services over the World Wide Web have contributed the most to the establishment of digital video as a necessary part of our lives. However, these developments have also inevitably resulted in a tremendous increase in the amount of video material created every day. This presents new possibilities for businesses and individuals alike. Business opportunities in particular include the development of applications for semantics-based retrieval of video content from the Internet, video stock agencies or personal collections; semantics-aware delivery of video content in desktop and mobile devices; and semantics-based video coding and transmission. Evidently, the above opportunities also reflect to the video manipulation possibilities offered to individual users. Besides opportunities, though, the abundance of digital video content also presents new and important technological challenges, which are crucial for the further development of the aforementioned innovative services. The cornerstone of the efficient manipulation of video material is the understanding of its underlying semantics, a goal that has long been identified as the “Holy grail of content-based media analysis research” (Chang, 2002). Efforts to understand the semantics of video content typically build on algorithms that operate at the signal level, such as temporal and spatiotemporal video segmentation algorithms that aim at partitioning a video stream into semantically meaningful parts. To support the goal of semantic analysis, these signal-level algorithms are augmented with a priori knowledge regarding the different semantic objects and events of interest that may appear in the video and their signal-level properties. The introduction of a priori knowledge serves the purpose of facilitating the detection and exploitation of the hidden associations between the signal and semantic levels, resulting in the generation of semantically meaningful metadata for the video content. In this article, existing state-of-the-art semantic video analysis and understanding techniques are reviewed, including a hybrid approach to semantic video analysis that is outlined in some more detail, and the future trends in this research area are identified. The literature presentation starts in the following section with signal level algorithms for processing video content, a necessary prerequisite for the subsequent application of knowledge-based techniques.
Chapter Preview


Segmentation is in general the process of partitioning a piece of information into meaningful elementary parts termed segments. Considering video, the term segmentation is used to describe a range of different processes for partitioning the video into meaningful parts at different granularities (Salembier & Marques, 1999). Segmentation of video can thus be temporal, aiming to break down the video to scenes or shots, spatial, addressing the problem of independently segmenting each video frame to arbitrarily shaped regions, or spatio-temporal, extending the previous case to the generation of temporal sequences of arbitrarily shaped spatial regions. The term segmentation is also frequently used to describe foreground/background separation in video, which can be seen as a special case of spatio-temporal segmentation. In any case, the application of any segmentation method is often preceded by a simplification step for discarding unnecessary information (e.g., low-pass filtering) and a feature extraction step for modifying or estimating features not readily available in the visual medium (e.g., texture, motion features, etc., but also color features in a different color space, etc.).

Key Terms in this Chapter

Compressed Video Segmentation: Segmentation of video without its prior decompression.

Ontology: Knowledge representation formalism, used for expressing explicit knowledge.

Temporal Video Segmentation: Partition the video to elementary image sequences termed shots, defined as a set of consecutive frames taken without interruption by a single camera.

Spatiotemporal Video Segmentation: Partition the video to elementary spatio-temporal objects, that is, sequences of temporally adjacent arbitrarily-shaped spatial regions.

Knowledge-Assisted Analysis: Analysis techniques making use of prior knowledge for the content being processed.

Machine Learning Techniques: Training-based techniques for discovering and representing implicit knowledge, such as complex relationships and interdependencies between numerical image data and perceptually higher-level concepts.

Semantic Video Analysis: Extraction of the semantics of the video, that is, detection and recognition of semantic objects and events.

Complete Chapter List

Search this Book: