Large-Scale Video Retrieval via Deep Local Convolutional Features

Large-Scale Video Retrieval via Deep Local Convolutional Features

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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 Cosmetic Bags 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 Elica NIKOLATESLA RC 83cm Recirculating Air Venting Induction Hob – BLACK 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.