Abstract: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.

Abstract: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.

Abstract: Low-dimensional structure from high-dimensional data is described for example, in the context of video foreground/background segmentation, speech signal background identification, document clustering and other applications where distortions in the observed data may exist. In various embodiments a first convex optimization process is used to find low dimensional structure from observations such as video frames in a manner which is robust to distortions in the observations; a second convex optimization process is used for incremental observations so bringing computational efficiency whilst retaining robustness. In various embodiments error checks are made to decide when to move between the first and second optimization processes. In various examples, the second convex optimization process encourages similarity between the solution it produces and the solution of the first convex optimization process, for example, by using an objective function which is suitable for convex optimization.

Abstract: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.

Abstract: The claimed subject matter provides a system and/or a method that facilitates generating sorted search results for a query. An interface component can receive a query in a first language. A first ranker can be trained from a portion of data related to a second language. A second ranker can correspond to the first language, wherein the second ranker is untrained due to a limited amount of data related to the first language. A sorting component can invoke the first ranker to generate and order a pre-defined number of search results for the received query and subsequently invoke the second ranker to the pre-defined number of search results to generate a re-ordered number of search results in the first language for the received query.

Abstract: Low-dimensional structure from high-dimensional data is described for example, in the context of video foreground/background segmentation, speech signal background identification, document clustering and other applications where distortions in the observed data may exist. In various embodiments a first convex optimization process is used to find low dimensional structure from observations such as video frames in a manner which is robust to distortions in the observations; a second convex optimization process is used for incremental observations so bringing computational efficiency whilst retaining robustness. In various embodiments error checks are made to decide when to move between the first and second optimization processes. In various examples, the second convex optimization process encourages similarity between the solution it produces and the solution of the first convex optimization process, for example, by using an objective function which is suitable for convex optimization.

Abstract: The claimed subject matter provides a system and/or a method that facilitates generating sorted search results for a query. An interface component can receive a query in a first language. A first ranker can be trained from a portion of data related to a second language. A second ranker can correspond to the first language, wherein the second ranker is untrained due to a limited amount of data related to the first language. A sorting component can invoke the first ranker to generate and order a pre-defined number of search results for the received query and subsequently invoke the second ranker to the pre-defined number of search results to generate a re-ordered number of search results in the first language for the received query.