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Image Retrieval with Structured Object Queries Using Latent Ranking SVM
Tian Lan1, Weilong Yang1, Yang Wang2, and Greg Mori1
1Simon Fraser University, Canada
tla58@cs.sfu.ca
wya16@cs.sfu.ca
mori@cs.sfu.ca
2University of Manitoba, Canada
ywang@cs.umanitoba.ca
Abstract. We consider image retrieval with structured object queries – queries that specify the objects that should be present in the scene, and their spatial relations. An example of such queries is “car on the road”. Existing image retrieval systems typically consider queries consisting of object classes (i.e. keywords). They train a separate classifier for each object class and combine the output heuristically. In contrast, we develop a learning framework to jointly consider object classes and their relations. Our method considers not only the objects in the query (“car” and “road” in the above example), but also related object categories can be useful for retrieval. Since we do not have ground-truth labeling of object bounding boxes on the test image, we represent them as latent variables in our model. Our learning method is an extension of the ranking SVM with latent variables, which we call latent ranking SVM. We demonstrate image retrieval and ranking results on a dataset with more than a hundred of object classes.
LNCS 7577, p. 129 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012