Abstract: Disclosed is a method comprising identifying one or more moving features in a first video via a computing platform, representing the one or more moving features as fingerprint representations via the computing platform, and comparing the fingerprint representations with fingerprint representations of a second video to determine a match via the computing platform.

Abstract: Disclosed are apparatus and methods for detecting whether a video is adult or non-adult. In certain embodiments, a learning system is operable to generate one or more models for adult video detection. The model is generated based on a large set of known videos that have been defined as adult or non-adult. Adult detection is then based on this adult detection model. This adult detection model may be applied to selected key frames of an unknown video. In certain implementations, these key frames can be selected from the frames of the unknown video. Each key frame may generally correspond to a frame that contains key portions that are likely relevant for detecting pornographic or adult aspects of the unknown video. By way of examples, key frames may include moving objects, skin, people, etc. In alternative embodiments, a video is not divided into key frames and all frames are analyzed by a learning system to generate a model, as well as by an adult detection system based on such model.

Abstract: Techniques are described herein for automatically evaluating the quality of digital images based on one or more color characteristics of the images. In some embodiments, a quality metric that indicates the likelihood that the digital images convey semantics is generated based on color characteristics of the digital images. The quality metric may be used, for example, to determine which keyframe to use to make a thumbnail to represent video data. In some embodiments, feature values are generated for an image based on color characteristics of the image, and the feature values are assigned to bins. In such embodiments, the quality metric may be generated to indicate how uniform the distribution of feature values is among the bins.

Abstract: Subject matter disclosed herein relates to a method of detecting video content, and in particular, to a method of defining one or more digital fingerprints to identify video content.

Abstract: Disclosed are apparatus and methods for detecting whether a video is adult or non-adult. In certain embodiments, a learning system is operable to generate one or more models for adult video detection. The model is generated based on a large set of known videos that have been defined as adult or non-adult. Adult detection is then based on this adult detection model. This adult detection model may be applied to selected key frames of an unknown video. In certain implementations, these key frames can be selected from the frames of the unknown video. Each key frame may generally correspond to a frame that contains key portions that are likely relevant for detecting pornographic or adult aspects of the unknown video. By way of examples, key frames may include moving objects, skin, people, etc. In alternative embodiments, a video is not divided into key frames and all frames are analyzed by a learning system to generate a model, as well as by an adult detection system based on such model.

Abstract: Techniques are described herein for automatically evaluating the quality of digital images based on one or more color characteristics of the images. In some embodiments, a quality metric that indicates the likelihood that the digital images convey semantics is generated based on color characteristics of the digital images. The quality metric may be used, for example, to determine which keyframe to use to make a thumbnail to represent video data. In some embodiments, feature values are generated for an image based on color characteristics of the image, and the feature values are assigned to bins. In such embodiments, the quality metric may be generated to indicate how uniform the distribution of feature values is among the bins.