Learning Global and Regional Features for Photo Annotation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Jiquan Ngiam , Hanlin Goh

ABSTRACT

This paper describes a method that learns a variety of features to perform photo annotation. We introduce concept-specific regional features and combine them with global features. The regional features were extracted through a novel region selection algorithm based on Multiple Instance Learning. Supervised classification for photo annotation was learned using Support Vector Machines with extended Gaussian Kernels over the χ2 distance, together with a simple greedy feature selection. The method was evaluated using the ImageCLEF 2009 Photo Annotation task and competitive benchmarking results were achieved. More... »

PAGES

287-290

References to SciGraph publications

Book

TITLE

Multilingual Information Access Evaluation II. Multimedia Experiments

ISBN

978-3-642-15750-9
978-3-642-15751-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_36

DOI

http://dx.doi.org/10.1007/978-3-642-15751-6_36

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034080535


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