Image clustering based on a shared nearest neighbors approach for tagged collections.
Résumé
Browsing and finding pictures in large-scale and heterogeneous collections is an important issue, most particularly for online photo sharing applications. Since such services know a huge growing of their database, the tag-based indexing strategy and the results displayed in a traditional “in a single file” representation are not efficient to browse and query image collections. Naturally, data clustering appeared as a good solution by presenting a summarized view of an image set instead of an exhaustive but useless list of its element. We present a new method for image clustering based on a shared nearest neighbors approach that could be processed on both content-based features and textual descriptions (tags). We describe, discuss and evaluate the SNN method for image clustering and present some experimental results using the Flickr collections showing that our approach provides useful representations of an image set.