TY - GEN
T1 - Retrieving and ranking unannotated images through collaboratively mining online search results
AU - Xu, Songhua
AU - Jiang, Hao
AU - Lau, Francis Chi Moon
PY - 2011
Y1 - 2011
N2 - We present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online image and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are utilized to estimate the reference images' relevance to the search query. The key feature of our method is its capability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, our algorithm infers the relevance of an online search result image to a text query. Once we obtain the estimate of query relevance score for each online image search result, we can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. We tested our algorithm both on the standard public image datasets and several modestly sized personal photo collections. We also compared our method with two well-known peer methods. The results indicate that our algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images.
AB - We present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online image and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are utilized to estimate the reference images' relevance to the search query. The key feature of our method is its capability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, our algorithm infers the relevance of an online search result image to a text query. Once we obtain the estimate of query relevance score for each online image search result, we can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. We tested our algorithm both on the standard public image datasets and several modestly sized personal photo collections. We also compared our method with two well-known peer methods. The results indicate that our algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images.
KW - multimedia information retrieval
KW - online reference image collection
KW - query processing
KW - retrieving unannotated images
KW - web information retrieval
KW - web search mining
UR - http://www.scopus.com/inward/record.url?scp=83055191902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83055191902&partnerID=8YFLogxK
U2 - 10.1145/2063576.2063650
DO - 10.1145/2063576.2063650
M3 - Conference contribution
AN - SCOPUS:83055191902
SN - 9781450307178
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 485
EP - 494
BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11
Y2 - 24 October 2011 through 28 October 2011
ER -