Multimodal learning for multi-label image classification

We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information...

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Bibliographic Details
Main Authors: Yanwei Pang, Zhao Ma, Yuan Yuan, Xuelong Li, Kongqiao Wang
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Feature extraction
Image classification
Kernel
Multilabel learning
Multimodal features
Multiple kernel learning
Semantics
Support vector machines
Visualization
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Description
Summary:We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel learning to combine visual and textual features to make a better image classification. In our experiments on PASCAL VOC'07 set and MIR Flickr set, we demonstrate the benefit of using multimodal feature to improve image classification. Specifically, we discover that on the issue of image classification, utilizing latent semantic feature to represent images and associated tags can obtain better classification results than other ways that integrating several low-level features.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2011.6115811