Keyframe extraction methods aim to find in a video sequence the most significant frames, according to specific criteria. In this paper we propose a new method to search, in a video database, for frames that arerelated to a given keyword, and to extract the best ones, according to a proposed quality factor. We first exploit a speech to text algorithm to extract automatic captions from all the video in a specific domaindatabase. Then we select only those sequences (clips), whose captions include a given keyword, thus discarding a lot of information that is useless for our purposes. Each retrieved clip is then divided into shots,using a video segmentation method, that is based on the SURF descriptors and keypoints. The sentence ofthe caption is projected onto the segmented clip, and we select the shot that includes the input keyword. Theselected shot is further inspected to find good quality and stable parts, and the frame which maximizes aquality metric is selected as the best and the most significant frame. We compare the proposed algorithm with another keyframe extraction method based on local features, in terms of Significance and Quality.
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2015|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition