PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Trans Media Relevance Feedback for Image Autoannotation
Thomas Mensink, Jakob Verbeek and Gabriela Csurka
In: BMVC 2010(2010).

Abstract

Automatic image annotation is an important tool for keyword-based image retrieval, providing a textual index for non-annotated images. Many image auto annotation methods are based on visual similarity between images to be annotated and images in a training corpus. The annotations of the most similar training images are transferred to the image to be annotated. In this paper we consider using also similarities among the training images, both visual and textual, to derive pseudo relevance models, as well as crossmedia relevance models. We extend a recent state-of-the-art image annotation model to incorporate this information. On two widely used datasets (COREL and IAPR) we show experimentally that the pseudo-relevance models improve the annotation accuracy.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8407
Deposited By:Jakob Verbeek
Deposited On:04 December 2011