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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chien, Been-Chian Ku, Chia-Wei |
| Abstract | Digital photos and images increase considerably in recent years. How to manage and access large-scale images efficiently and effectively becomes an important issue in big data era. The main techniques of image retrieval are typically divides into two categories: the content-based retrieval approach and the keyword-based retrieval approach. The content-based retrieval approach searches images with a similar query image. In other hand, the keyword-based retrieval approach searches images with keywords. For retrieving images by semantic keywords and avoiding manually annotation by users, high-accurate image classification is required. In this paper, we propose a hybrid framework combining classifiers, similarity matching and association rules for large-scale multi-label image classification. The results of experiments show that the combination model provides an effective image classification. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450337359 |
| DOI | 10.1145/2818869.2818900 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-10-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text mining Image annotation Similarity matching Image classification |
| Content Type | Text |
| Resource Type | Article |
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