Abstract: A system for generating an advertisement is provided. The system may receive an advertisement request from a client device and select an advertisement from a database in response to the advertisement request. The system may identify an advertiser web server associated with the advertisement, for example a landing page. The system may retrieve a picture from the advertiser web server and integrate the picture with the advertisement to generate an enhanced advertisement. The system may serve the enhanced advertisement to the client device.

Abstract: A system for generating an advertisement is provided. The system may receive an advertisement request from a client device and select an advertisement from a database in response to the advertisement request. The system may identify an advertiser web server associated with the advertisement, for example a landing page. The system may retrieve a picture from the advertiser web server and integrate the picture with the advertisement to generate an enhanced advertisement. The system may serve the enhanced advertisement to the client device.

Abstract: A system for generating an advertisement is provided. The system may receive an advertisement request from a client device and select an advertisement from a database in response to the advertisement request. The system may identify an advertiser web server associated with the advertisement, for example a landing page. The system may retrieve a picture from the advertiser web server and integrate the picture with the advertisement to generate an enhanced advertisement. The system may serve the enhanced advertisement to the client device.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically selecting the most definitive, precise and high-quality content files corresponding to POIs. The disclosed systems and methods utilize the performance of visual comparisons with a set of definitive content files of a given POI, and by incorporating visual aesthetic features as a factor of such comparisons, a search result is identified that down-weights imprecise and poor quality content files of a given POI, and ensures that only high quality, accurate content files are selected or identified.

Abstract: Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Abstract: Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically selecting the most definitive, precise and high-quality content files corresponding to POIs. The disclosed systems and methods utilize the performance of visual comparisons with a set of definitive content files of a given POI, and by incorporating visual aesthetic features as a factor of such comparisons, a search result is identified that down-weights imprecise and poor quality content files of a given POI, and ensures that only high quality, accurate content files are selected or identified.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: A system for generating an advertisement is provided. The system may receive an advertisement request from a client device and select an advertisement from a database in response to the advertisement request. The system may identify an advertiser web server associated with the advertisement, for example a landing page. The system may retrieve a picture from the advertiser web server and integrate the picture with the advertisement to generate an enhanced advertisement. The system may serve the enhanced advertisement to the client device.

Abstract: Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Abstract: Briefly, embodiments of methods and/or systems of detecting and image of a human face in a digital image are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be refined by a neural network to generate signal sample value levels corresponding to probability that a human face may be depicted at a localized region of a digital image.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: Briefly, embodiments of methods and/or systems of detecting and image of a human face in a digital image are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be refined by a neural network to generate signal sample value levels corresponding to probability that a human face may be depicted at a localized region of a digital image.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Abstract: The proposed Dynamic Motion Vector Analysis method applies to the motion compensation module of a video decoder system. The method analyzes the motion-vectors of a given region of picture frame and outputs a set of regions to be fetched from the reference frames stored in the external memory. The size and number of regions are decided by a hierarchical method that uses a set of user-defined input thresholds. Pre-processing of the motion vectors associated with the given region allows the method to handle reference data to be fetched from multiple reference frames in the same framework. A complementary dynamic batch (region of operation) size strategy that works along with MV-analysis is also proposed to help utilize the on-chip memory resources more efficiently.

Abstract: The proposed Dynamic Motion Vector Analysis method applies to the motion compensation module of a video decoder system. The method analyzes the motion-vectors of a given region of picture frame and outputs a set of regions to be fetched from the reference frames stored in the external memory. The size and number of regions are decided by a hierarchical method that uses a set of user-defined input thresholds. Pre-processing of the motion vectors associated with the given region allows the method to handle reference data to be fetched from multiple reference frames in the same framework. A complementary dynamic batch (region of operation) size strategy that works along with MV-analysis is also proposed to help utilize the on-chip memory resources more efficiently.