Automating the construction of scene classifiers for content-based video retrieval


Israël, Menno and Broek, Egon L. van den and Putten, Peter van der (2004) Automating the construction of scene classifiers for content-based video retrieval. In: Fifth International Workshop on Multimedia Data Mining, MDM/KDD 2004, 22 August 2004, Seattle, WA, USA (pp. pp. 38-47).

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Abstract:This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering.
Item Type:Conference or Workshop Item
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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