Abstract
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 overall 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.
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Basili, R., Petitti, R., Saracino, D. (2007). LSA-Based Automatic Acquisition of Semantic Image Descriptions. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds) Semantic Multimedia. SAMT 2007. Lecture Notes in Computer Science, vol 4816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77051-0_4
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DOI: https://doi.org/10.1007/978-3-540-77051-0_4
Publisher Name: Springer, Berlin, Heidelberg
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