18-04-2012, 04:20 PM
Temporal video segmentation
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INTRODUCTION
Recent advances in multimedia compression technology, coupled with the significant increase in computer performance and the growth of Internet, have led to the widespread use and availability of digital video. Applications such as digital libraries, distance learning, video-on-demand, digital video broadcast, interactive TV, multimedia information systems generate and use large collections of video data. This has created a need for tools that can efficiently index, search, browse and retrieve relevant material. Consequently, several content-based retrieval systems for organizing and managing video databases have been recently proposed [8,26,34].
TEMPORAL VIDEO SEGMENTATION
More than eight years of temporal video segmentation research have resulted in a great variety of
algorithms. Early work focus on cut detection, while more recent techniques deal with the harder
problem - gradual transitions detection.
Temporal Video Segmentation in Uncompressed Domain
The majority of algorithms process uncompressed video. Usually, a similarity measure between successive images is defined. When two images are sufficiently dissimilar, there may be a cut. Gradual transitions are found by using cumulative difference measures and more sophisticated.
Pixel Comparison
Pair-wise pixel comparison (also called template matching) evaluates the differences in intensity or color values of corresponding pixels in two successive frames.
The simplest way is to calculate the absolute sum of pixel differences and compare it against a threshold [18]:
Histogram comparison
A step further towards reducing sensitivity to camera and object movements can be done by
comparing the histograms of successive images. The idea behind histogram-based approaches is
that two frames with unchanging background and unchanging (although moving) objects will have
little difference in their histograms. In addition, histograms are invariant to image rotation and
change slowly under the variations of viewing angle and scale [35]. As a disadvantage one can note
that two images with similar histograms may have completely different content. However, the
probability for such events is low enough, moreover techniques for dealing with this problem have
already been proposed in [28].
CONCLUSION
Temporal video segmentation is the first step towards automatic annotation of digital video for browsing and retrieval. It is an active area of research gaining attention from several research communities including image processing, computer vision, pattern recognition and artificial intelligence.