Temporal Aggregation of Visual Features for Large-Scale Image-to-Video Retrieval

In this research we study the specific task of image-to-video retrieval, in which static pictures are used to find a specific timestamp or frame within a collection of videos. The inner temporal structure of video data consists of a sequence of highly correlated images or frames, commonly reproduced at rates of 24 to 30 frames per second. To perform large-scale retrieval, it is necessary to reduce the amount of data to be processed by exploiting the redundancy between these highly correlated images. In this work, we explore several techniques to aggregate visual temporal information from video data based on both standard local features and deep learning representations with the focus on the image-to-video retrieval task.