Image Indexing Using Prosemantic Features

We present here, an image description approach based on prosemantic features. The images are firstly represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. In this paper we will show how prosemantic features outperform traditional low-level features in a variety of tasks. One is content-based retrieval: we included prosemantic features into the framework of the QuickLook2 image retrieval system. Target search experiments show that the use of prosemantic features, combined with the relevance feedback mechanism of QuickLook2, allows for a more successful and quick retrieval of the query images with respect to low-level features. Moreover, we will show the effectiveness of our features for the browsing and visualization of the results obtained from image search engines.