Abstract: Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Abstract: Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Abstract: Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Abstract: Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Abstract: Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating image search results. One of the methods includes receiving first image search results responsive to a text query, each first image search result associated with a respective first score indicating a relevance of an image represented by the first image search result to the text query. Second image search results responsive to a query image are received, each second image search result associated with a respective second score indicating a measure of similarity between an image represented by the second image search result and the query image. A set of final image search results is selected including combining first scores and second scores of the selected first image search results. The final image search results are ordered by similarity to the query image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating image search results. One of the methods includes receiving first image search results responsive to a text query, each first image search result associated with a respective first score indicating a relevance of an image represented by the first image search result to the text query. Second image search results responsive to a query image are received, each second image search result associated with a respective second score indicating a measure of similarity between an image represented by the second image search result and the query image. A set of final image search results is selected including combining first scores and second scores of the selected first image search results. The final image search results are ordered by similarity to the query image.

Abstract: A computer system creates a plurality of objects using facts derived from electronic documents, each object including one or more facts describing an entity associated with the object. The system generates a value for an object of an unknown entity type, of the plurality of objects, by using an entity type model for a known entity type. The entity type model is based on a set of features of a plurality of objects of the known entity type, and the value indicates whether the object of an unknown entity type is of the known entity type. The system assigns the known entity type to the object of an unknown entity type in response to a determination that the value indicates the object of an unknown entity type is of the known entity type, and stores the object with the assigned entity type.

Abstract: A repository contains objects including facts about entities. Objects may be of known or unknown entity type. An entity type assignment engine assigns entity types to objects of unknown entity type. A feature generation module generates a set of features describing the facts included with each object in the repository. An entity type model module generates an entity type model based on the sets of features generated for a subset of objects. An entity type model module generates entity type models, such as a classifier or generative models, based on the sets of features associated with objects of known entity type. An entity type assignment module generates a value based on the sets of features associated with an object of unknown entity type and the entity type model. This value indicates whether the object of unknown entity type is of a known entity type. An object update module stores the object to which the known entity type was assigned in the repository in association with the assigned entity type.

Abstract: A repository contains objects including facts about entities. Some objects might be associated with the same entity. An object merge engine identifies a set of merge candidate objects. A grouping module groups the merge candidate objects based on the values of facts included in the objects. An object comparison module compares pairs of objects in each group to identify evidence for and/or against merging the pair. Evidence for merging the pair exists if, e.g., the objects have a type in common or share an uncommon fact. Evidence against merging the pair exists if, e.g., the objects have differing singleton attributes. A graph generation module generates graphs describing the evidence for and/or against merging the pair. A merging module analyzes the graphs and merges objects associated with the same entity. The merged objects are stored in the repository.

Abstract: A method for navigating digital medical images includes providing a digitized patient medical image of a structure of interest in a patient, using a pre-trained classifier to segment the structure of interest from the image, creating links from the structure of interest to a corresponding structure in an anatomical atlas, receiving a query to view the structure of interest, parsing the query to identify one or more keywords from noun phrases in the query, mapping a keyword to a corresponding structure in the anatomical atlas, wherein the anatomical atlas structure is associated with a link to the corresponding structure in the patient image, and following the link to display said patient structure of interest.

Abstract: A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.

Abstract: A method for navigating digital medical images includes providing a digitized patient medical image of a structure of interest in a patient, using a pre-trained classifier to segment the structure of interest from the image, creating links from the structure of interest to a corresponding structure in an anatomical atlas, receiving a query to view the structure of interest, parsing the query to identify one or more keywords from noun phrases in the query, mapping a keyword to a corresponding structure in the anatomical atlas, wherein the anatomical atlas structure is associated with a link to the corresponding structure in the patient image, and following the link to display said patient structure of interest.

Abstract: A user interface (UI) for adaptive video fast forward provides a novel fully adaptive content-based UI for allowing user interaction with an image sequence or video relative to a user identified query sample. This query sample is drawn either from an image sequence being searched or from another image sequence entirely. The user interaction offered by the UI includes providing a user with computationally efficient searching, browsing and retrieval of one or more objects, frames or sequences of interest in video or image sequences, as well as automatic content-based variable-speed playback based on a computed similarity to the query sample. In addition, the UI also provides the capability to search for image frames or sequences that are dissimilar to the query sample, thereby allowing the user to quickly locate unusual or different activity within an image sequence.

Abstract: Computationally efficient searching, browsing and retrieval of one or more objects in a video sequence are accomplished using learned generative models. The generative model is trained on an automatically or manually selected query sequence from a sequence of image frames. The resulting generative model is then used in searching, browsing or retrieval of one or more similar or dissimilar image frames or sequences within the image sequence by determining the likelihood of each frame under the learned generative model. Further, this method allows for automatic separation and balancing of various causes of variability while analyzing the image sequence. The generative models are based on appearances of multiple, possibly occluding objects in an image sequence. Further, the search strategies used include clustering and intelligent fast forward through the image sequence. Additionally, in one embodiment, a fast forward speed is relative to the current frame likelihood under the learned generative model.

Abstract: A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.

Abstract: A user interface (UI) for adaptive video fast forward provides a novel fully adaptive content-based UI for allowing user interaction with an image sequence or video relative to a user identified query sample. This query sample is drawn either from an image sequence being searched or from another image sequence entirely. The user interaction offered by the UI includes providing a user with computationally efficient searching, browsing and retrieval of one or more objects, frames or sequences of interest in video or image sequences, as well as automatic content-based variable-speed playback based on a computed similarity to the query sample. In addition, the UI also provides the capability to search for image frames or sequences that are dissimilar to the query sample, thereby allowing the user to quickly locate unusual or different activity within an image sequence.

Abstract: Computationally efficient searching, browsing and retrieval of one or more objects in a video sequence are accomplished using learned generative models. The generative model is trained on an automatically or manually selected query sequence from a sequence of image frames. The resulting generative model is then used in searching, browsing or retrieval of one or more similar or dissimilar image frames or sequences within the image sequence by determining the likelihood of each frame under the learned generative model. Further, this method allows for automatic separation and balancing of various causes of variability while analyzing the image sequence. The generative models are based on appearances of multiple, possibly occluding objects in an image sequence. Further, the search strategies used include clustering and intelligent fast forward through the image sequence. Additionally, in one embodiment, a fast forward speed is relative to the current frame likelihood under the learned generative model.