HAL - INRIA :: [inria-00548632, version 1] Trans Media Relevance Feedback for Image Autoannotation

inria-00548632, version 1

Trans Media Relevance Feedback for Image Autoannotation

Thomas Mensink ()^a^, 1, 2, 3, Jakob Verbeek () ², Gabriela Csurka ¹

British Machine Vision Conference (BMVC '10) (2010) 20.1--20.12

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.

a – INRIA, XRCE
1: Xerox Research Centre Europe (XRCE)
Xerox
2: LEAR (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
CNRS : FR71 – CNRS : UMR5527 – INRIA – Laboratoire Jean Kuntzmann – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
3: Laboratoire Jean Kuntzmann (LJK)
CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology

inria-00548632, version 1
http://hal.inria.fr/inria-00548632
oai:hal.inria.fr:inria-00548632
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Submitted for: Laure BENAC <>
Submitted on: Monday, 20 December 2010 10:22:16
Updated on: Friday, 1 July 2011 09:31:27

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DOI: 10.5244/C.24.20

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