TY - GEN
T1 - Improving content-based image retrieval by identifying least and most correlated visual words
AU - Kaliciak, Leszek
AU - Song, Dawei
AU - Wiratunga, Nirmalie
AU - Pan, Jeff
PY - 2012
Y1 - 2012
N2 - In this paper, we propose a model for direct incorporation of image content into a (short-term) user profile based on correlations between visual words and adaptation of the similarity measure. The relationships between visual words at different contextual levels are explored. We introduce and compare various notions of correlation, which in general we will refer to as image-level and proximity-based. The information about the most and the least correlated visual words can be exploited in order to adapt the similarity measure. The evaluation, preceding an experiment involving real users (future work), is performed within the Pseudo Relevance Feedback framework. We test our new method on three large data collections, namely MIRFlickr, ImageCLEF, and a collection from British National Geological Survey (BGS). The proposed model is computationally cheap and scalable to large image collections.
AB - In this paper, we propose a model for direct incorporation of image content into a (short-term) user profile based on correlations between visual words and adaptation of the similarity measure. The relationships between visual words at different contextual levels are explored. We introduce and compare various notions of correlation, which in general we will refer to as image-level and proximity-based. The information about the most and the least correlated visual words can be exploited in order to adapt the similarity measure. The evaluation, preceding an experiment involving real users (future work), is performed within the Pseudo Relevance Feedback framework. We test our new method on three large data collections, namely MIRFlickr, ImageCLEF, and a collection from British National Geological Survey (BGS). The proposed model is computationally cheap and scalable to large image collections.
KW - Content-based image retrieval and representation
KW - Correlation
KW - Local features
KW - Pseudo relevance feedback
KW - Similarity measure
UR - http://www.scopus.com/inward/record.url?scp=84871585169&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35341-3_27
DO - 10.1007/978-3-642-35341-3_27
M3 - Published conference contribution
AN - SCOPUS:84871585169
SN - 9783642353406
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 316
EP - 325
BT - Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
T2 - 8th Asia Information Retrieval Societies Conference, AIRS 2012
Y2 - 17 December 2012 through 19 December 2012
ER -