Internet tourism scene classification with multi-feature fusion and transfer learning
Internet tourism scene classification with multi-feature fusion and transfer learning
- Author(s): Jie Liu ; Junping Du ; Xiaoru Wang
- DOI: 10.1049/cp.2011.0768
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- Author(s): Jie Liu ; Junping Du ; Xiaoru Wang Source: IET International Conference on Communication Technology and Application (ICCTA 2011), 2011 p. 747 – 751
- Conference: IET International Conference on Communication Technology and Application (ICCTA 2011)
- DOI: 10.1049/cp.2011.0768
- ISBN: 978-1-84919-470-9
- Location: Beijing, China
- Conference date: 14-16 Oct. 2011
- Format: PDF
This paper proposes an internet tourism scene classification algorithm, named multi-feature fusion with transfer learning, which utilizes unlabeled auxiliary data to facilitate image classification. Firstly, we do the SURF extraction and MRHM analysis for the training data separately, in which the training data set as combined with labeled images and unlabeled auxiliary images. Then we compute the target feature vector for each image by merging the extended SURF descriptor and MRHM feature. Finally, we train the SVM classifier scene classification. Due to the capability of transferring knowledge, the proposed algorithm can effectively address insufficient training data problem for image classification. Experiments are conducted on a Beijing tourism scene dataset to evaluate the performance of our proposed algorithm. The experimental results are encouraging and promising.
Inspec keywords: learning (artificial intelligence); feature extraction; statistical analysis; support vector machines; Internet; image fusion; travel industry; image classification
Subjects: Computer vision and image processing techniques; Administration of other service industries; Other topics in statistics; Optical, image and video signal processing; Information networks; Other topics in statistics; Knowledge engineering techniques
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