Real-time feature extraction from video stream data for stream ...

Real-time feature extraction from video stream data for stream ...

6. Feature Extraction

few examples. The second level of the hierarchy then of course depends on the first level.

For ”video:” we have decided to specify next, whether the feature is related to a single

frame (”frame:”), a sliding windows of n frames (”n-frames:”) or a whole show (”show:”).

As difference images of two successive frames are useful for cut detection, the ”2-frame:”-

features are especially interesting. Furthermore we hope to be able to calculate them very

quick by taking advantage of the MPEG-codex (see chapter 2.2.1). For ”video:frame”-

features the next level of the hierarchy than describes the color channel the feature is

gained from. Image 6.1 shows an extract from the full naming space for features used in

my diploma thesis. This proposal has been adopted for the ViSTA-TV project.

Figure 6.1.: The naming space for features.

6.2. Image Feature Extraction with RapidMiner

The RapidMiner Image Mining Extension (see 3.5.2) provides a bunch of useful operators

for extracting features from single images. These include operators for image transformation,

filtering and feature extraction. Furthermore it comes with an operator, allowing

us to loop over all images contained in one folder (Multiple Color Image Opener). As we

are able to decode a movie file and store all images in one folder by using xuggler (see

5.2), we can not only use the Image Mining Extension to extract features from images,

but from video data as well. In the following section 6.2.1, I first of all describe the

IMMI operators used for the feature extraction. Afterwards the whole feature extraction

process in RapidMiner is explained.


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