The paper describes a technique that supports efficient and effective Content-Based Image Retrieval (CBIR) in very large image archives as well as automatic image tagging. The proposed technique uses a unified representation for image visual features and for image textual descriptions. Images are clustered according to their image visual features while textual content is used to associate relevant tags to images belonging to the same cluster. The system supports image retrieval based on image query similarity, on textual queries, and on mixed mode queries composed of an image and a textual part and automatic image tagging.

Enriching image feature description supporting effective content-based retrieval and annotation

Debole F;Gennaro C;Savino P
2014

Abstract

The paper describes a technique that supports efficient and effective Content-Based Image Retrieval (CBIR) in very large image archives as well as automatic image tagging. The proposed technique uses a unified representation for image visual features and for image textual descriptions. Images are clustered according to their image visual features while textual content is used to associate relevant tags to images belonging to the same cluster. The system supports image retrieval based on image query similarity, on textual queries, and on mixed mode queries composed of an image and a textual part and automatic image tagging.
2014
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
9781479972272
Information Storage and Retrieval
Pattern Recognition
Similarity Search
Content Based Image Retrieval
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Descrizione: Preprint - Enriching image feature description supporting effective content-based retrieval and annotation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/302925
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