Relevance-Based Image Selection

Abstract

A system, computer readable storage medium, and computer-implemented method presents video search results responsive to a user keyword query. The video hosting system uses a machine learning process to learn a feature-keyword model associating features of media content from a labeled training dataset with keywords descriptive of their content. The system uses the learned model to provide video search results relevant to a keyword query based on features found in the videos. Furthermore, the system determines and presents one or more thumbnail images representative of the video using the learned model.

Description

BACKGROUND[0001]1. Field of the Art[0002]The invention relates generally to identifying videos or their parts that are relevant to search terms. In particular, embodiments of the invention are directed to selecting one or more representative thumbnail images based on the audio-visual content of a video.[0003]2. Background[0004]Users of media hosting websites typically browse or search the hosted media content by inputting keywords or search terms to query textual metadata describing the media content. Searchable metadata may include, for example, titles of the media files or descriptive summaries of the media content. Such textual metadata often is not representative of the entire content of the video, particularly when a video is very long and has a variety of scenes. In other words, if a video has a large number of scenes and variety of content, it is likely that some of those scenes are not described in the textual metadata, and as a result, that video would not be returned in re...

AI Technical Summary

Benefits of technology

[0009]Advantageously, the video hosting system finds and presents search results based on the actual content of the videos instead of relying solely on textual metadata. Thus, the video hosting system enables the user to better assess the relevance of videos in the set of search results.

Problems solved by technology

Such textual metadata often is not representative of the entire content of the video, particularly when a video is very long and has a variety of scenes.

Thus, conventional search engines often fail to return the media content most relevant to the user's search.

A second problem with conventional media hosting websites is that due to the large amount of hosted media content, a search query may return hundreds or even thousands of media files responsive to the user query.

Consequently, the user may have difficulties assessing which of the hundreds or thousands of search results are most relevant.

However, a thumbnail selected in this manner is often not representative of the actual content of the video, since there is no relationship between the ordinal position of the thumbnail and the content of a video.

Furthermore, the thumbnail may not be relevant to the user's search query.

Thus, the user may have difficulty assessing which of the hundreds or thousands of search results are most relevant.

Images