Abstract
In this paper, we propose a novel attempt of model-free image annotation which annotates images by mining their search results. It contains three steps: 1) the search process to discover visually and semantically similar search results; 2) the mining process to identify salient terms from textual descriptions of the search results; and 3) the annotation rejection process to filter out noisy terms yielded by step 2). To ensure real time annotation, two key techniques are leveraged - one is to map the high dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Our proposed approach enables annotating with unlimited vocabulary, and is highly scalable and robust to outliers. Experimental results on both real web images and a bench mark image dataset show the effectiveness and efficiency of the proposed algorithm. © 2008 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 1919-1932 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 30 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Clustering
- Information filtering
- Object recognition
- Real-time systems