Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive nearest neighbor search for relevance feedback in large image databases
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Searching in metric spaces with user-defined and approximate distances
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
The GC-tree: a high-dimensional index structure for similarity search in image databases
IEEE Transactions on Multimedia
FISH: a practical system for fast interactive image search in huge databases
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
3D motion retrieval based on double index and user interaction
International Journal of Information and Communication Technology
BNCOD'13 Proceedings of the 29th British National conference on Big Data
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Image retrieval has found more and more applications. Due to the well recognized semantic gap problem, the accuracy and the recall of image similarity search are often still low. As an effective method to improve the quality of image retrieval, the relevance feedback approach actively applies users' feedback to refine the search. As searching a large image database is often costly, to improve the efficiency, high dimensional indexes may help. However, many existing database indexes are not adaptive to updates of distance measures caused by users' feedback. In this paper, we propose a demo to illustrate the relevance feedback based interactive images retrieval procedure, and examine the effectiveness and the efficiency of various indexes. Particularly, audience can interactively investigate the effect of updated distance measures on the data space where the images are supposed to be indexed, and on the distributions of the similar images in the indexes. We also introduce our new B+-tree-like index method based on cluster splitting and iDistance.