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SOM-Hunter: Video Browsing With Relevance-to-SOM Feedback Loop

Publication at Faculty of Mathematics and Physics |
2020

Abstract

This paper presents a prototype video retrieval engine focusing on a simple known-item search workflow, where users initialize the search with a query and then use an iterative approach to explore a larger candidate set. Specifically, users gradually observe a sequence of displays and provide feedback to the system.

The displays are dynamically created by a self organizing map that employs the scores based on the collected feedback, in order to provide a display matching the user preferences. In addition, users can inspect various other types of specialized displays for exploitation purposes, once promising candidates are found.