Abstract. In this paper an application of the TS-SOM variant of the self-organising map algorithm on the problem of copyright theft detection for bitmap images is shown. The algorithm facilitates the location of originals of copied, damaged or modified images within a database of hundreds of thousands of stock images. The method is shown to outperform binary decision tree indexing with invariant frame detection.

Two-dimensional similarity-based image organizing studies how to place photos within 2D virtual canvas based on their visual contents so that the users can easily locate the desired photos. As an extension to our previous work [10], several improvements are made in this paper to allow better photo browsing experiences. For example, the new approach pre-orders all the photos so that a consistent set of photos is selected for display. This solves the photo flickering problem of our previous approach, which uses K-mean algorithm to dynamically select photos. The main focus of this paper however is on the evaluation of the effectiveness of different feature vectors for 2D photo organization. A performance metric is proposed to measure how well photos with similar visual contents are grouped together on the 2D canvas. Feature vectors generated using eight different low-level feature extraction approaches are tested. The evaluation results reveal the pros and cons of different feature extraction approaches, which can be a useful guide for developing new feature vectors.