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Proceedings Paper

How good are the visual MPEG-7 features?
Author(s): Horst Eidenberger
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Paper Abstract

The study presented in this paper analyses descriptions extracted with MPEG-7-descriptors from visual content from the statistical point of view. Good descriptors should generate descriptions with high variance, a well-balanced cluster structure and high discriminance to be able to distinguish different media content. Statistical analysis reveals the quality of the used description extraction algorithms. This was not considered in the MPEG-7-design process where optimising the recall was the major goal. For the analysis eight basic visual descriptors were applied on three media collections: the Brodatz dataset (monochrome textures), a selection of the Corel dataset (colour photos) and a set of coats-of-arms images (artificial colour images with few colour gradations). The results were analysed with four statistical methods: mean and variance of descriptor elements, distribution of elements, cluster analysis (hierarchical and topological) and factor analysis. The main results are: The best descriptors for combination are Color Layout, Dominant Color, Edge Histogram and Texture Browsing. The other are highly dependent on these. The colour histograms (Color Structure and Scalable Color) perform badly on monochrome input. Generally, all descriptors are highly redundant and the application of complexity reduction transformations could save up to 80% of storage and transmission capacity.

Paper Details

Date Published: 23 June 2003
PDF: 13 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.503064
Show Author Affiliations
Horst Eidenberger, Technische Univ. Wien (Austria)


Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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