![]() |
FeedbackBypass | |||
| ||||
FeedbackBypass is a technique able to improve the overall performance of image similarity searches by learning user preferences that determine the similarity criterion and providing adequate approximate answers to trade-off the quality of results for execution speed. The basic idea consists in properly maintaining user preferences (i.e. relevance feedback) from user interactions (i.e. feedback loops) to either "bypass" the feedback loop completely for already-seen queries, or to "predict" near-optimal similarity criterion for new queries.
Two different implementations of FeedbackBypass have been developed: A wavelet-based version that defines a structure called "Simplex Tree", and a second one that uses Support Vector Machines (SVM). |
||||
|