Video summarization is a powerful tool for video understanding and browsing and is considered as an enabler for many video analysis tasks. While the effect of video summarization models has been largely studied in video retrieval and indexing applications over the last decade, its impact has not been well investigated in content-based video recommendation systems (RSs) based on low-level visual features, where the goal is to recommend items/videos to users based on visual content of videos. This work reveals specific problems related to video summarization and their impact on video recommendation. We present preliminary results of an analysis involving applying different video summarization models for the problem of video recommendation on a real-world RS dataset (MovieLens-10M) and show how temporal feature aggregation and video segmentation granularity can significantly influence/improve the quality of recommendation.

The effect of different video summarization models on the quality of video recommendation based on low-level visual features

DELDJOO, YASHAR;CREMONESI, PAOLO;QUADRANA, MASSIMO
2017-01-01

Abstract

Video summarization is a powerful tool for video understanding and browsing and is considered as an enabler for many video analysis tasks. While the effect of video summarization models has been largely studied in video retrieval and indexing applications over the last decade, its impact has not been well investigated in content-based video recommendation systems (RSs) based on low-level visual features, where the goal is to recommend items/videos to users based on visual content of videos. This work reveals specific problems related to video summarization and their impact on video recommendation. We present preliminary results of an analysis involving applying different video summarization models for the problem of video recommendation on a real-world RS dataset (MovieLens-10M) and show how temporal feature aggregation and video segmentation granularity can significantly influence/improve the quality of recommendation.
2017
ACM International Conference Proceeding Series - Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing (CBMI)
9781450353335
Content-based video recommendation, temporal feature summarization, feature aggregation, shot segmentation granularity, evaluation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1031989
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