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2016

Fisher Kernel Temporal Variation-based Relevance Feedback for video retrieval

4 years 3 months ago
Fisher Kernel Temporal Variation-based Relevance Feedback for video retrieval
This paper proposes a novel framework for Relevance Feedback based on the Fisher Kernel (FK). Specifically, we train a Gaussian Mixture Model (GMM) on the top retrieval results (without supervision) and use this to create a FK representation, which is therefore specialized in modelling the most relevant examples. We use the FK representation to explicitly capture temporal variation in video via frame-based features taken at different time intervals. While the GMM is being trained, a user selects from the top examples those which he is looking for. This feedback is used to train a Support Vector Machine on the FK representation, which is then applied to re-rank the top retrieved results. We show that our approach outperforms other state-of-the-art relevance feedback methods. Experiments were carried out on the Blip10000, UCF50, UCF101 and ADL standard datasets using a broad range of multi-modal content descriptors (visual, audio, and text).
Ionut Mironica, Bogdan Ionescu, Jasper R. R. Uijli
Added 01 Apr 2016
Updated 01 Apr 2016
Type Journal
Year 2016
Where CVIU
Authors Ionut Mironica, Bogdan Ionescu, Jasper R. R. Uijlings, Nicu Sebe
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