Abstract
Image categorization is very helpful for organizing large image databases efficiently, however, it is yet very challenging due to lack of effective image representations. Our previous work showed that structural representations were good at characterizing image contents, since image contents could be exploited from coarse to fine scales through the structures representation and fewer visual features are required. In this paper, several popular image partition methods are investigated for adaptive image categorization based on structural representation. Experimental results on seven categories of scenery images show that both the structure and node attributes are important to categorize image contents. In addition, the more similar the structures of each category, the better the categorization performance.
| Original language | English |
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| Title of host publication | 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) |
| Pages | 676-680 |
| Number of pages | 5 |
| Volume | 1 |
| Publication status | Published - 1 Dec 2004 |
| Event | 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Kunming, China Duration: 6 Dec 2004 → 9 Dec 2004 |
Conference
| Conference | 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) |
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| Country/Territory | China |
| City | Kunming |
| Period | 6/12/04 → 9/12/04 |
ASJC Scopus subject areas
- Engineering(all)