Semantic image retrieval system based on object relationships
| dc.contributor.author | Shivakumar, S. | |
| dc.contributor.author | Goel, N. | |
| dc.contributor.author | Ananthanarayana, V.S. | |
| dc.contributor.author | Cyriac, C. | |
| dc.contributor.author | Rajaram, P. | |
| dc.date.accessioned | 2026-02-06T06:40:02Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Semantic-based image retrieval has recently become popular as an avenue to improve retrieval accuracy. The 'semantic gap' between the visual features and the high-level semantic features could be narrowed down by utilizing this kind of retrieval method. However, most of the current methods of semantic-based image retrieval utilize visual semantic features and do not consider spatial relationships. We build a system for content-based image retrieval from image collections on the web and tackle the challenges of distinguishing between images that contain similar objects, in order to capture the semantic meaning of a search query. In order to do so, we utilize a combination of segmentation into objects as well as the relationships of these objects with each other. © 2013 IEEE. | |
| dc.identifier.citation | 2013 IEEE 2nd International Conference on Image Information Processing, IEEE ICIIP 2013, 2013, Vol., , p. 276-281 | |
| dc.identifier.uri | https://doi.org/10.1109/ICIIP.2013.6707598 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/32681 | |
| dc.subject | image segmentation | |
| dc.subject | Scale Invariant Feature Transform (SIFT) | |
| dc.subject | semantic gap | |
| dc.subject | Semantic image retrieval | |
| dc.title | Semantic image retrieval system based on object relationships |
