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- Publisher Website: 10.1145/1631272.1631479
- Scopus: eid_2-s2.0-72449210182
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Conference Paper: T-IRS: Textual query based image retrieval system for consumer photos
Title | T-IRS: Textual query based image retrieval system for consumer photos |
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Authors | |
Keywords | Cross domain learning Text based photo retrieval |
Issue Date | 2009 |
Citation | MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums, 2009, p. 983-984 How to Cite? |
Abstract | In this demonstration, we present a (quasi) real-time textual query based image retrieval system (T-IRS) for consumer photos by leveraging millions of web images and their associated rich textual descriptions (captions, categories, etc.). After a user provides a textual query (e.g., "boat"), our system automatically finds the positive web images that are related to the textual query "boat" as well as the negative web images which are irrelevant to the textual query. Based on these automatically retrieved positive and negative web images, we employ the decision stump ensemble classifier to rank personal consumer photos. To further improve the photo retrieval performance, we also develop a novel relevance feedback method, referred to as Cross-Domain Regularized Regression (CDRR), which effectively utilizes both the web images and the consumer images. Our system is inherently not limited by any predefined lexicon. |
Persistent Identifier | http://hdl.handle.net/10722/321393 |
DC Field | Value | Language |
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dc.contributor.author | Liu, Yiming | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Tsang, Ivor W. | - |
dc.contributor.author | Luo, Jiebo | - |
dc.date.accessioned | 2022-11-03T02:18:37Z | - |
dc.date.available | 2022-11-03T02:18:37Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums, 2009, p. 983-984 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321393 | - |
dc.description.abstract | In this demonstration, we present a (quasi) real-time textual query based image retrieval system (T-IRS) for consumer photos by leveraging millions of web images and their associated rich textual descriptions (captions, categories, etc.). After a user provides a textual query (e.g., "boat"), our system automatically finds the positive web images that are related to the textual query "boat" as well as the negative web images which are irrelevant to the textual query. Based on these automatically retrieved positive and negative web images, we employ the decision stump ensemble classifier to rank personal consumer photos. To further improve the photo retrieval performance, we also develop a novel relevance feedback method, referred to as Cross-Domain Regularized Regression (CDRR), which effectively utilizes both the web images and the consumer images. Our system is inherently not limited by any predefined lexicon. | - |
dc.language | eng | - |
dc.relation.ispartof | MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums | - |
dc.subject | Cross domain learning | - |
dc.subject | Text based photo retrieval | - |
dc.title | T-IRS: Textual query based image retrieval system for consumer photos | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/1631272.1631479 | - |
dc.identifier.scopus | eid_2-s2.0-72449210182 | - |
dc.identifier.spage | 983 | - |
dc.identifier.epage | 984 | - |