Abstract: Disclosed herein is an intelligent agent to analyze a media object. The agent comprises a trained model comprising a number of state layers for storing a history of actions taken by the agent in each of a number of previous iterations performed by the agent in analyzing a media object. The stored state may be used by the agent in a current iteration to determine whether or not to make, or abstain from making, a prediction from output generated by the model, identify another portion of the media object to analyze, end analysis. Output from the agent's model may comprise a semantic vector that can be mapped to a semantic vector space to identify a number of labels for a media object.

Abstract: One or more computing devices, systems, and/or methods for selective content insertion into areas of media objects are provided. For example, a media object (e.g., an image or video), is selected for composition with content, such as where a message, interactive content, a hyperlink, or other types of content is overlaid or embedded into the media object to create a composite media object. The content is added into an area of the media object that is selectively identified to reduce occlusion and/or improve visual cohesiveness between the content and the media object (e.g., added to an area with a similar or complimentary color, having an adequate size with spare amounts of visual features such as a soccer player, a ball, or other entity, etc.). In this way, the content may be add into the area of the media object to create a composite media object to provide to users.

Abstract: Disclosure includes system, method and architecture for selecting supplemental digital content using visual appearance. Digital content that is visually similar, or dissimilar, to digital content requested by a user can he identified and provided for presentation with the requested content. The additional digital content is visually congruent, or visually incongruent, with content requested by a user, such that the additional content is similar, or dissimilar, to the requested content from a visual standpoint. In a presentation of the requested content, the presentation position of each additional content item relative to the presentation position of each requested content can be identified using visual congruence, or visual incongruence.

Abstract: Disclosed herein is an intelligent agent to analyze a media object. The agent comprises a trained model comprising a number of state layers for storing a history of actions taken by the agent in each of a number of previous iterations performed by the agent in analyzing a media object. The stored state may be used by the agent in a current iteration to determine whether or not to make, or abstain from making, a prediction from output generated by the model, identify another portion of the media object to analyze, end analysis. Output from the agent's model may comprise a semantic vector that can be mapped to a semantic vector space to identify a number of labels for a media object.

Abstract: Disclosed herein is an intelligent agent to analyze a media object. The agent comprises a trained model comprising a number of state layers for storing a history of actions taken by the agent in each of a number of previous iterations performed by the agent in analyzing a media object. The stored state may be used by the agent in a current iteration to determine whether or not to make, or abstain from making, a prediction from output generated by the model, identify another portion of the media object to analyze, end analysis. Output from the agent's model may comprise a semantic vector that can be mapped to a semantic vector space to identify a number of labels for a media object.

Abstract: Disclosed herein is an intelligent agent to analyze a media object. The agent comprises a trained model comprising a number of state layers for storing a history of actions taken by the agent in each of a number of previous iterations performed by the agent in analyzing a media object. The stored state may be used by the agent in a current iteration to determine whether or not to make, or abstain from making, a prediction from output generated by the model, identify another portion of the media object to analyze, end analysis. Output from the agent's model may comprise a semantic vector that can be mapped to a semantic vector space to identify a number of labels for a media object.

Abstract: Images that comprise text are identified and output from the images is generated wherein the output comprises text from the image in textual data format. The portions of an image comprising the text data are initially identified and the text imaged by the pixels of that image portion is extracted in textual data format. The extracted text is stored so that a search for images comprising particular text is enabled.