The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Clustering through ranking on manifolds
ICML '05 Proceedings of the 22nd international conference on Machine learning
Relevance feedback based on query refining and feature database updating in CBIR system
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Interactive image search by 2D semantic map
Proceedings of the 19th international conference on World wide web
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Botanical data retrieval system supporting discovery learning
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Interactive Image Search by Color Map
ACM Transactions on Intelligent Systems and Technology (TIST)
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Image search is becoming prevalent in web search as the number of digital photos grows exponentially on the internet. For a successful image search system, removing outliers in the top ranked results is a challenging task. Typical content based image search engines take an input image from one class as a query and compute relevance between the query and images in a database. The results often contain a large number of outliers, since these outliers may be similar to the query image in some way. In this paper we present a novel search scheme using query images from multiple classes. Instead of conducting query search for one image class at a time, we conduct multi-class query search jointly. By using several query classes that are similar to each other for multi-class query, we can utilize information across similar classes to fine tune the similarity measure to remove outliers. This strategy can be used for any information search application. In this work, we use content based image search to illustrate the concept.