Share Email Print

Proceedings Paper

Application of discriminative models for interactive query refinement in video retrieval
Author(s): Amit Srivastava; Saurabh Khanwalkar; Anoop Kumar
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The ability to quickly search for large volumes of videos for specific actions or events can provide a dramatic new capability to intelligence agencies. Example-based queries from video are a form of content-based information retrieval (CBIR) where the objective is to retrieve clips from a video corpus, or stream, using a representative query sample to find more like this. Often, the accuracy of video retrieval is largely limited by the gap between the available video descriptors and the underlying query concept, and such exemplar queries return many irrelevant results with relevant ones. In this paper, we present an Interactive Query Refinement (IQR) system which acts as a powerful tool to leverage human feedback and allow intelligence analyst to iteratively refine search queries for improved precision in the retrieved results. In our approach to IQR, we leverage discriminative models that operate on high dimensional features derived from low-level video descriptors in an iterative framework. Our IQR model solicits relevance feedback on examples selected from the region of uncertainty and updates the discriminating boundary to produce a relevance ranked results list. We achieved 358% relative improvement in Mean Average Precision (MAP) over initial retrieval list at a rank cutoff of 100 over 4 iterations. We compare our discriminative IQR model approach to a naïve IQR and show our model-based approach yields 49% relative improvement over the no model naïve system.

Paper Details

Date Published: 24 December 2013
PDF: 6 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671N (24 December 2013); doi: 10.1117/12.2051887
Show Author Affiliations
Amit Srivastava, Raytheon BBN Technologies (United States)
Saurabh Khanwalkar, Raytheon BBN Technologies (United States)
Anoop Kumar, Raytheon BBN Technologies (United States)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top