Abstract: Disclosed are apparatus and methods for providing machine-learning services. A context-identification system executing on a mobile platform can receive data comprising context-related data associated with the mobile platform and application-related data received from the mobile platform. The context-identification system can identify a context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform. Based on at least one context identified, context-identification system can predict a communicative action associated with the mobile platform by performing a machine-learning operation on the received data. An instruction can be received to execute the communicative action associated with the mobile platform.

Abstract: Implementations relate to a computer-implemented method and a device for determining a relative posed between devices. The method can include receiving data representing first keypoint features from a first image captured by a camera of a second mobile computing device; capturing, by a camera of a first mobile computing device, a second image, wherein the first image and the second image comprise a substantially common scene having an area of overlap; computing, by the first mobile computing device, data representing second keypoint features from the second image; determining, by the first mobile computing device, based at least in part on the data representing first keypoint features and the data representing second keypoint features, a relative pose of the first mobile computing device and the second mobile computing device; and communicating the relative pose to the second mobile computing device.

Abstract: Methods and apparatus for predicting time spans for mobile platform activation are presented. A machine-learning service executing on a mobile platform receives feature-related data. The feature-related data includes usage-related data about time spans that the mobile platform is activated and platform-related data received from the mobile platform. The usage-related data and the platform-related data can differ. The machine-learning service determines whether the machine-learning service is trained to perform machine-learning operations related to predicting a time span that the mobile platform will be activated. In response to determining that the machine-learning service is trained, the machine-learning service: receives a request for a predicted time span that the mobile platform will be activated, determines the predicted time span by the machine-learning service performing a machine-learning operation on the feature-related data, and sends the predicted time span.

Abstract: Disclosed are apparatus and methods for providing machine-learning services. A machine-learning service executing on a mobile platform can receive data related to a plurality of features. In some cases, the received data can include data related to features received from an application and data related to features received from the mobile platform. The machine-learning service can determine at least one feature based on the received data. The machine-learning service can generate an output by performing a machine-learning operation on the at least one feature. The machine-learning operation can be selected from among an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature. The machine-learning service can send the output.

Abstract: A concept learning module trains video classifiers associated with a stored set of concepts derived from textual metadata of a plurality of videos, the training based on features extracted from training videos. Each of the video classifiers can then be applied to a given video to obtain a score indicating whether or not the video is representative of the concept associated with the classifier. The learning process does not require any concepts to be known a priori, nor does it require a training set of videos having training labels manually applied by human experts. Rather, in one embodiment the learning is based solely upon the content of the videos themselves and on whatever metadata was provided along with the video, e.g., on possibly sparse and/or inaccurate textual metadata specified by a user of a video hosting service who submitted the video.

Abstract: Methods and systems for generating playlists of media items with audio data are disclosed. Based on two received feature sets, media items corresponding to each feature set are identified. Transition characteristics are also received. Based on the identified media items and transition characteristics, a dynamic playlist is generated that transitions from media items having characteristics of the first feature set to media items having characteristics of the second feature set. Each time the playlist is generated, it may include a different set of media items.

Abstract: Methods and systems for generating playlists of media items with audio data are disclosed. Based on two received feature sets, media items corresponding to each feature set are identified. Transition characteristics are also received. Based on the identified media items and transition characteristics, a dynamic playlist is generated that transitions from media items having characteristics of the first feature set to media items having characteristics of the second feature set. Each time the playlist is generated, it may include a different set of media items.

Abstract: Methods and systems for generating playlists of media items with audio data are disclosed. Based on two received feature sets, media items corresponding to each feature set are identified. Transition characteristics are also received. Based on the identified media items and transition characteristics, a dynamic playlist is generated that transitions from media items having characteristics of the first feature set to media items having characteristics of the second feature set. Each time the playlist is generated, it may include a different set of media items.

Abstract: An apparatus and a concomitant method for rectification and recognition of symbols (e.g., to correct for the effects of perspective distortion, rotation and/or scale) in images of three-dimensional scenes is provided. The present method locates a reference region lying in a common plane with a symbol to be recognized, where the reference region represents an image of a planar object having assumed (e.g., known or standard) geometry and dimensions, along with at least four easily detectable correspondence points within that geometry. An image of the common plane is then rectified in three dimensions in accordance with the assumed dimensions of the reference region in order to produce a transformed (e.g., rectified) image of the symbol, which is significantly easier to recognize than the original imaged appearance of the symbol using existing recognition methods.

Abstract: A method and apparatus are provided for person identification. In one embodiment, a method for identifying an individual includes obtaining at least one image of the individual, where the image depicts at least a portion of the individual's hair, comparing the visual characteristics of the individual's hair to the visual characteristics of imaged hair in one or more stored images, and identifying the individual based on the comparison.

Abstract: A method and apparatus are provided for analyzing an electronic communication containing imagery, e.g., to determine whether or not the electronic communication is a spam communication. In one embodiment, an inventive method includes detecting one or more regions of imagery in a received electronic communication and applying pre-processing techniques to locate regions (e.g., blocks or lines) of text in the imagery that may be distorted. The method then analyzes the regions of text to determine whether the content of the text indicates that the electronic communication is spam. In one embodiment, specialized extraction and rectification of embedded text followed by optical character recognition processing is applied to the regions of text to extract their content therefrom. In another embodiment, keyword recognition or shape-matching processing is applied to detect the presence or absence of spam-indicative words from the regions of text.

Abstract: An apparatus and a concomitant method for rectification and recognition of symbols (e.g., to correct for the effects of perspective distortion, rotation and/or scale) in images of three-dimensional scenes is provided. The present method locates a reference region lying in a common plane with a symbol to be recognized, where the reference region represents an image of a planar object having assumed (e.g., known or standard) geometry and dimensions, along with at least four easily detectable correspondence points within that geometry. An image of the common plane is then rectified in three dimensions in accordance with the assumed dimensions of the reference region in order to produce a transformed (e.g., rectified) image of the symbol, which is significantly easier to recognize than the original imaged appearance of the symbol using existing recognition methods.

Abstract: The present invention relates to a method for determining the orientation of text, so that the text can be correctly read. In one embodiment, the method analyzes the “open” portions of text characters to determine in which direction the open portions face. By determining the respective densities of characters opening in each direction (e.g., right or left), the method can establish in which direction the text as a whole is orientated. The present invention may be adapted for use in automated mail processing, to determine the orientation of checks in automated teller machine envelopes, or the orientation of scanned or copied documents, or documents sent via facsimile, or to determine the orientation of digital photographs that include text (e.g., road signs, business cards, driver's licenses, etc.), among other applications. A fast, substantially automatic determination of text orientation according to the present invention will reduce the labor expense and processing delay required for manual orientation.