Abstract: A method for displaying video streams involves detecting multiple video events, and displaying multiple video streams on a display. Display windows for displayed video streams define an ordered sequence of display windows. When a new event is detected, the display of one or more of the video streams is shifted to respective next one or more display windows in an ordered sequence of display windows.

Abstract: Methods, systems, and techniques for performing a facet search include receiving facet search commencement user input indicating that a search for a facet is to commence; in response to the facet search commencement user input, searching one or more video recordings for the facet; and displaying, on a display, facet image search results depicting the facet, wherein the facet image search results are selected from the one or more video recordings. An artificial neural network may be used for the facet search, and that network may be trained by generating a facet image training set that comprises training images, with the training images depicting a type of facet common to the training images; and training, by using the facet image training set, that neural network to classify the type of facet when a sample image comprising the type of facet is input to that network.

Abstract: Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.

Abstract: A method for displaying video streams involves detecting multiple video events, and displaying multiple video streams on a display. Display windows for displayed video streams define an ordered sequence of display windows. When a new event is detected, the display of one or more of the video streams is shifted to respective next one or more display windows in an ordered sequence of display windows.

Abstract: Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.

Abstract: Multiple natural language training text strings are obtained. For example, text portions may be randomly selected and converted into natural language text based on one or more randomly selected rules. A formatted training text string is generated for each natural language training text string, for example using a context-free grammar parser. The formatted training text strings are inputted to a machine learning model. For each formatted training text string, using the machine learning model, a natural language text string is generated. The natural language text string is associated with one of the natural language training text strings. One or more parameters of the machine learning model are adjusted based on one or more differences between at least one of the natural language text strings and its associated natural language training text string.

Abstract: A method of selecting content object tags for recommendation to a user includes having a taxonomy of tags, with a plurality of tag taxonomy branches, for labeling content objects to be stored at a content hosting service; identifying a baseline subset of content objects based on a user context at the content hosting service; identifying a targeted subset of the baseline subset of content objects based on the user context, wherein each content object in the targeted subset of content objects is associated with one or more tags; determining a tag score for each tag associated with the targeted subset of content objects; determining a maximum number of tags to be recommended to the user based on available space within a user interface of the user; selecting tags with the highest recommendation score from a number of different tag taxonomy branches, as recommended tags to the user, the number being no more than the maximum number; receiving tags selected by the user from among the recommended tags; and performing

Abstract: A method for displaying video streams involves detecting multiple video events, and displaying multiple video streams on a display. Display windows for displayed video streams define an ordered sequence of display windows. When a new event is detected, the display of one or more of the video streams is shifted to respective next one or more display windows in an ordered sequence of display windows.

Abstract: A method of selecting content object tags for recommendation to a user includes having a taxonomy of tags for labeling content objects to be stored at a content hosting service; identifying a baseline subset of content objects based on a user context at the content hosting service; identifying a targeted subset of the baseline subset based on the user context; determining a tag score for each tag associated with the targeted subset of content objects; determining a maximum number of tags to be recommended to the user based on available space within a user interface of the user; selecting tags with the highest recommendation score from a number of different tag taxonomy branches, the number being no more than the maximum number; receiving tags selected by the user from among the recommended tags; and performing an operation on the content object corpus for the user context using the selected tags.

Abstract: Multiple natural language training text strings are obtained. For example, text portions may be randomly selected and converted into natural language text based on one or more randomly selected rules. A formatted training text string is generated for each natural language training text string, for example using a context-free grammar parser. The formatted training text strings are inputted to a machine learning model. For each formatted training text string, using the machine learning model, a natural language text string is generated. The natural language text string is associated with one of the natural language training text strings. One or more parameters of the machine learning model are adjusted based on one or more differences between at least one of the natural language text strings and its associated natural language training text string.

Abstract: There are described methods and systems for facilitating identification of an object-of-interest. A face similarity score and a body similarity score of a query image relative to a gallery image are determined. A fused similarity score of the query image relative to the gallery image is determined by applying a relationship between the face similarity score, the body similarity score, and the fused similarity score. The fused similarity score is indicative of whether or not the object-of-interest and the potential object-of-interest are the same object-of-interest. For example, a machine learning process is used to fuse the face similarity score and the body similarity into the fused similarity score. The process is repeated for multiple gallery images. The gallery images may then be ranked according to their respective fused similarity scores.

Abstract: Methods, systems, and techniques for performing a hash-based appearance search. A processor is used to obtain a hash vector that represents a search subject that is depicted in an image. The hash vector includes one or more hashes as a respective one or more components of the hash vector. The processor determines which one or more of the hashes satisfy a threshold criterion and which one or more of the components of the hash vector qualify as a scoring component. The one or more components that qualify correspond to a respective one or more hashes that satisfy the threshold criterion and that are represented in a scoring database that is generated based on different examples of a search target. The processor determines a score representing a similarity of the search subject to the different examples of the search target.

Abstract: A method for displaying video streams involves detecting multiple video events, and displaying multiple video streams on a display. Display windows for displayed video streams define an ordered sequence of display windows. When a new event is detected, the display of one or more of the video streams is shifted to respective next one or more display windows in an ordered sequence of display windows.

Abstract: Methods, systems, and techniques for performing a hash-based appearance search. A processor is used to obtain a hash vector that represents a search subject that is depicted in an image. The hash vector includes one or more hashes as a respective one or more components of the hash vector. The processor determines which one or more of the hashes satisfy a threshold criterion and which one or more of the components of the hash vector qualify as a scoring component. The one or more components that qualify correspond to a respective one or more hashes that satisfy the threshold criterion and that are represented in a scoring database that is generated based on different examples of a search target. The processor determines a score representing a similarity of the search subject to the different examples of the search target.

Abstract: Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.

Abstract: Methods, systems, and techniques for performing a facet search include receiving facet search commencement user input indicating that a search for a facet is to commence; in response to the facet search commencement user input, searching one or more video recordings for the facet; and displaying, on a display, facet image search results depicting the facet, wherein the facet image search results are selected from the one or more video recordings. An artificial neural network may be used for the facet search, and that network may be trained by generating a facet image training set that comprises training images, with the training images depicting a type of facet common to the training images; and training, by using the facet image training set, that neural network to classify the type of facet when a sample image comprising the type of facet is input to that network.

Abstract: Collections identifying associated content objects, such as user-defined collections, are stored. A request to display or play a seed content object is received. A plurality of seed collections are identified based on the seed object. A plurality of candidate content objects are identified based on the identified seed collections, for instance by identifying the content objects that are identified by the seed collections. Properties of the seed object are identified, such as the categorization of the seed object, or the tags associated with the seed object. The candidate objects are ranked based on the identified seed collections and the identified properties of the seed object, and a candidate object is selected for recommendation to the user based on the ranking. The ranking of candidate objects can also be determined based on the coherence of the seed collections and the centrality of the seed object to the seed collections.

Abstract: A method of selecting content object tags for recommendation to a user includes having a taxonomy of tags, with a plurality of tag taxonomy branches, for labeling content objects to be stored at a content hosting service; identifying a baseline subset of content objects based on a user context at the content hosting service; identifying a targeted subset of the baseline subset of content objects based on the user context, wherein each content object in the targeted subset of content objects is associated with one or more tags; determining a tag score for each tag associated with the targeted subset of content objects; determining a maximum number of tags to be recommended to the user based on available space within a user interface of the user; selecting tags with the highest recommendation score from a number of different tag taxonomy branches, as recommended tags to the user, the number being no more than the maximum number; receiving tags selected by the user from among the recommended tags; and performing

Abstract: There are described methods and systems for facilitating identification of an object-of-interest. A face similarity score and a body similarity score of a query image relative to a gallery image are determined. A fused similarity score of the query image relative to the gallery image is determined by applying a relationship between the face similarity score, the body similarity score, and the fused similarity score. The fused similarity score is indicative of whether or not the object-of-interest and the potential object-of-interest are the same object-of-interest. For example, a machine learning process is used to fuse the face similarity score and the body similarity into the fused similarity score. The process is repeated for multiple gallery images. The gallery images may then be ranked according to their respective fused similarity scores.

Abstract: Content object tags at a content hosting service are used to classify stored content objects. Tags and clusters of tags (groups of one or more associated tags) can be recommended to a user of the content hosting service based on a user context, such as the browsing, viewing, uploading, or searching of content objects. Tags are scored based on content objects tagged with the tags in a targeted subset of content objects and a baseline subset of content objects, and based on the relevance of the content objects tagged with the tags. These tag scores can be weighted, and one or more tags can be selected for recommendation based on the weighted tag scores. Tag clusters can be selected for recommendation using a cluster hierarchy and determining whether a targeted subset of tags occur within a maximum number of tag clusters at a particular hierarchy level.