Content-based retrieval systems are becoming increasingly relevant for managing large multimedia databases, such as those found on the Internet. In this paper, we investigate applying content-based retrieval with relevance feedback to the popular YouTube human action dataset, using a variety of methods to extract and compare features, in order to determine the most accurate techniques in this setting. Among other techniques, we explore soft-assignment code-book clustering, feature pruning, motion and static features, Adaboost and ABRS-SVM for relevance feedback. We evaluate the performance of several different systems to find the best combination of techniques for human action retrieval. We demonstrate that existing relevance feedback methods do not work well for YouTube media, and that a naive algorithm consistently outperforms these.
|Publication status||Published - Aug 2011|
|Event||ICIMCS '11 - The Third International Conference on Internet Multimedia Computing and Service - Chengdu, China|
Duration: 1 Aug 2011 → …
|Conference||ICIMCS '11 - The Third International Conference on Internet Multimedia Computing and Service|
|Period||1/08/11 → …|