Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The nature of statistical learning theory
The nature of statistical learning theory
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Web multimedia documents are characterized by visual and linguistic information expressed by structured pages of images and texts. The suitable combinations able to generalize semantic aspects of the over-all multimedia information clearly depend on applications. In this paper, an unsupervised image classification technique combining features from different media levels is proposed. In particular linguistic descriptions derived through Information Extraction from Web pages are here integrated with visual features by means of Latent Semantic Analysis. Although the higher expressivity increases the complexity of the learning process, the dimensionality reduction implied by LSA makes it largely applicable. The evaluation over an image classification task confirms that the proposed model outperforms other methods acting on the individual levels. The resulting method is cost-effective and can be easily applied to semi-automatic image semantic labeling tasks as foreseen in collaborative annotation scenarios.