Classifying multimedia documents by merging textual and pictorial information
In this paper, we study computational models and techniques to merge textual and image features to classify multimedia documents into semantically meaningful groups. A vector-based framework is used to index documents on the basis of textual, pictorial and composite (textual-pictorial) information. The scheme makes use of weighted document terms and color invariant image features to obtain a high-dimensional image descriptor in vector form to be used as an index. Based on supervised learning, a classifier is used to organize the multimedia documents. Due to space limitations, in this paper, we focus on the application of classifying/finding pictures of people on the Internet. Performance evaluations are reported on the accuracy of merging textual and pictorial information for classification.