Waseda participated in the TRECVID 2015 Semantic Indexing (SIN) task . For the SIN task, our approach used the following processing pipelines: feature extraction using several deep convolutional neural networks (CNNs); classification of the presence or absence of a detection target by support vector machines (SVMs); and fusion of multiple score outputs. In order to improve the performance of semantic video indexing, we employed the following techniques: utilizing multiple evidences observed in each video and compressing them into a fixed-length vector; introducing gradient and motion features to CNNs; enriching variations of the training and the testing sets; and extracting features from several CNNs trained with various large-scale datasets. Through these techniques, our best run achieved a mean Average Precision (mAP) of 30.9%. This was ranked 2nd among all the participants.
|出版ステータス||Published - 2015|
|イベント||2015 TREC Video Retrieval Evaluation, TRECVID 2015 - Gaithersburg, United States|
継続期間: 2015 11 16 → 2015 11 18
|Conference||2015 TREC Video Retrieval Evaluation, TRECVID 2015|
|Period||15/11/16 → 15/11/18|
ASJC Scopus subject areas