TY - JOUR A2 - Reisslein, Martin AU - Zhang, Chen AU - Hu, Bin AU - Suo, Yucong AU - Zou, Zhiqiang AU - Ji, Yimu PY - 2020 DA - 2020/06/09 TI - Large-Scale Video Retrieval via Deep Local Convolutional Features SP - 7862894 VL - 2020 AB - In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods. SN - 1687-5680 UR - https://doi.org/10.1155/2020/7862894 DO - 10.1155/2020/7862894 JF - Advances in Multimedia PB - Hindawi KW - ER -