Improving video concept detection using uploader model

Visual concept detection is a very active field of research, motivated by the increasing amount of digital video available. While most systems focus on the processing of visual features only, in the context of internet videos other metadata is available which may provide useful information. In this paper, we investigate the role of the uploader information, the person who uploaded the video. We propose a simple uploader model which includes some knowledge about the content of videos uploaded by a given user. On the TRECVID 2012 Semantic Indexing benchmark [1], we show that this simple model is able to improve the concept detection score of all the 2012 participants, even the best ones, by only re-ranking the proposed shots. We also present some statistics which show that even though most TRECVID systems are based on visual features only, they provide results which are biased in favor of test videos for which the uploader was present in the development data. This work suggests further research on the use of metadata for visual concept detection, and a different way of organizing benchmark data to assess the visual performance of detectors.