Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2009

Abstract

In content-based image retrieval, the “semantic gap” between visual image features and user semantics makes it hard to predict abstract image categories from low-level features. We present a hybrid system that integrates global features (Gfeatures) and region features (R-features) for predicting image semantics. As an intermediary between image features and categories, we introduce the notion of mid-level concepts, which enables us to predict an image’s category in three steps. First, a G-prediction system uses G-features to predict the probability of each category for an image. Simultaneously, a R-prediction system analyzes R-features to identify the probabilities of mid-level concepts in that image. Finally, our hybrid H-prediction system based on a Bayesian network reconciles the predictions from both R-prediction and G-prediction to produce the final classifications. Results of experimental validations show that this hybrid system outperforms both Gprediction and R-prediction significantly

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2009 IEEE international conference on Multimedia and Expo, New York, June 28 - July 3

First Page

1

Last Page

4

ISBN

9781424442904

Identifier

10.1109/ICME.2009.5202554

Publisher

IEEE

City or Country

New York

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