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Multifeature analysis and semantic context learning for image classification

Multifeature analysis and semantic context learning for image classification Multifeature Analysis and Semantic Context Learning for Image Classification QIANNI ZHANG and EBROUL IZQUIERDO, Queen Mary, University of London This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this context model is used to infer object classes within images. Selected results from a comprehensive experimental evaluation are reported to show the effectiveness of the proposed approaches. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Retrieval models; I.4.8 [Image Processing and Computer Vision]: Scene Analysis--Object recognition General Terms: Algorithms Additional Key Words and Phrases: Image classification, object detection, multifeature fusion, semantic context modeling ACM Reference Format: Zhang, Q. and Izquierdo, E. 2013. Multifeature analysis and semantic context learning for image classification. ACM Trans. Multimedia Comput. Commun. Appl. 9, 2, Article 12 (May 2013), 20 pages. DOI: http://dx.doi.org/10.1145/2457450.2457454 1. INTRODUCTION http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Multifeature analysis and semantic context learning for image classification

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1551-6857
DOI
http://dx.doi.org/10.1145/2457450.2457454
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Abstract

Multifeature Analysis and Semantic Context Learning for Image Classification QIANNI ZHANG and EBROUL IZQUIERDO, Queen Mary, University of London This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this context model is used to infer object classes within images. Selected results from a comprehensive experimental evaluation are reported to show the effectiveness of the proposed approaches. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Retrieval models; I.4.8 [Image Processing and Computer Vision]: Scene Analysis--Object recognition General Terms: Algorithms Additional Key Words and Phrases: Image classification, object detection, multifeature fusion, semantic context modeling ACM Reference Format: Zhang, Q. and Izquierdo, E. 2013. Multifeature analysis and semantic context learning for image classification. ACM Trans. Multimedia Comput. Commun. Appl. 9, 2, Article 12 (May 2013), 20 pages. DOI: http://dx.doi.org/10.1145/2457450.2457454 1. INTRODUCTION

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: May 1, 2013

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