The Effectiveness of High-Level Text Features in SOM-based Web Image Clustering


The KIPS Transactions:PartB , Vol. 13, No. 2, pp. 121-126, Apr. 2006
10.3745/KIPSTB.2006.13.2.121,   PDF Download:

Abstract

In this paper, we propose an approach to increase the power of clustering Web images by using high-level semantic features from text information relevant to Web images as well as low-level visual features of image itself. These high-level text features can be obtained from image URLs and file names, page titles, hyperlinks, and surrounding text. As a clustering engine, self-organizing map (SOM) proposed by Kohonen is used. In the SOM-based clustering using high-level text features and low-level visual features, the 200 images from 10 categories are divided in some suitable clusters effectively. For the evaluation of clustering powers, we propose simple but novel measures indicating the degrees of scattering images from the same category, and degrees of accumulation of the same category images. From the experiment results, we find that the high-level text features are more useful in SOM-based Web image clustering.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
S. S. Cho, "The Effectiveness of High-Level Text Features in SOM-based Web Image Clustering," The KIPS Transactions:PartB , vol. 13, no. 2, pp. 121-126, 2006. DOI: 10.3745/KIPSTB.2006.13.2.121.

[ACM Style]
Soo Sun Cho. 2006. The Effectiveness of High-Level Text Features in SOM-based Web Image Clustering. The KIPS Transactions:PartB , 13, 2, (2006), 121-126. DOI: 10.3745/KIPSTB.2006.13.2.121.