Abstract: In accordance with one embodiment, a method can be implemented that comprises receiving as an input a title of a video from a video sharing web site; parsing the title of the video into one or more n-grams; computing with a computer a title-searchability-score by utilizing the one or more n-grams.

Abstract: In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.

Abstract: In accordance with one embodiment, a method can be implemented that comprises receiving as an input a title of a video from a video sharing web site; parsing the title of the video into one or more n-grams; computing with a computer a title-searchability-score by utilizing the one or more n-grams.

Abstract: In accordance with one embodiment, a method can be implemented that includes receiving a plurality of digital assets as inputs; extracting one or more representative features of each digital asset; for a group based on one or more representative features, using the one or more representative features of each digital asset to calculate a relevancy score between each digital asset and the group; and using each relevancy score to determine whether each digital asset should be assigned to the group. Additional embodiments are also disclosed herein.

Abstract: In accordance with one embodiment, a method can be implemented that comprises receiving as an input a title of a video from a video sharing web site; parsing the title of the video into one or more n-grams; computing with a computer a title-searchability-score by utilizing the one or more n-grams.

Abstract: In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.

Abstract: In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.

Abstract: In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.

Abstract: In accordance with one embodiment, an intelligent supplemental search engine optimization tool may generate keywords relating to content based on additional content collected from one or more data sources, wherein the data sources are selected based on initial input relating to the initial content. Data sources may include one or more third-party resources. A variety of processes may be employed to recommend keywords, including frequency-based and probabilistic-based recommendation processes.

Abstract: In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.