Patent application number | Description | Published |
20090252413 | IMAGE CLASSIFICATION - Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images. | 10-08-2009 |
20090316986 | FEATURE SELECTION AND EXTRACTION - Image feature selection and extraction (e.g., for image classifier training) is accomplished in an integrated manner, such that higher-order features are merely developed from first-order features selected for image classification. That is, first-order image features are selected for image classification from an image feature pool, initially populated with pre-extracted first-order image features. The selected first-order classifying features are paired with previously selected first-order classifying features to generate higher-order features. The higher-order features are placed into the image feature pool as they are developed or “on-the-fly” (e.g., for use in image classifier training). | 12-24-2009 |
20120141020 | IMAGE CLASSIFICATION - Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images. | 06-07-2012 |
20130024448 | RANKING SEARCH RESULTS USING FEATURE SCORE DISTRIBUTIONS - Document features or document ranking values can be associated with a distribution of values. Feature values, feature value coefficients, and/or document ranking values can be generated based on sampled values from the distribution of values. This can allow the relative ranking of a document to vary. As additional information is obtained regarding the document, leading to greater certainty about the appropriate ranking of the document, the width or variation generated by the distribution can be reduced to provide more stable ranking values | 01-24-2013 |
20150055856 | IMAGE CLASSIFICATION - Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images. | 02-26-2015 |
Patent application number | Description | Published |
20090018980 | MULTIPLE-INSTANCE PRUNING FOR LEARNING EFFICIENT CASCADE DETECTORS - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
20090018981 | LEARNING CLASSIFIERS USING COMBINED BOOSTING AND WEIGHT TRIMMING - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
20090018985 | HISTOGRAM-BASED CLASSIFIERS HAVING VARIABLE BIN SIZES - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
20110314011 | AUTOMATICALLY GENERATING TRAINING DATA - Computer-readable media, computer systems, and computing devices facilitate generating binary classifier and entity extractor training data. Seed URLs are selected and URL patterns within the seed URLs are identified. Matching URLs in a data structure are identified and corresponding queries and their associated weights are added to a potential training data set from which training data is selected. | 12-22-2011 |
20140181204 | INTEREST GRAPH-POWERED SEARCH - A method, which identifies information of interest within an organization, determines use data that characterizes relationships among information items within the organization, where the information items include user data and collections of information items. The method generates interest data indicating affinities among the information items based on the determined use data. After receiving a query for data regarding the information items, the method responds to the query by providing one or more results based on the generated interest data. More details are provided herein. | 06-26-2014 |
20140278816 | INTEREST GRAPH-POWERED SHARING - Techniques for organizing information, such as documents, presentations, web sites and web pages, audiovisual media streams, and the like are describe. This disclosed techniques include creating and using an interest graph to support the sharing of information via search, browsing, and discovery, etc. and measuring consumption, engagement, and/or influence based on that information. An interest graph expresses the affinity between people and information—the likelihood that a particular piece of information is of interest to a particular person. The interest graph is based on an understanding of relationships, monitoring of user behavior, and analysis of each piece of information. The interest graph represents many kinds of relationships, including: between users and other users, users and items, and users and collections. The interest graph can be computed using data both from a set of items and from user behavior. | 09-18-2014 |
20140280120 | INTEREST GRAPH-POWERED BROWSING - Techniques for organizing information, such as documents, presentations, web sites and web pages, audiovisual media streams, and the like are describe. This disclosed techniques include creating and using an interest graph to assist in a user's browsing of information. An interest graph expresses the affinity between people and information—the likelihood that a particular piece of information is of interest to a particular person. The interest graph is based on an understanding of relationships, monitoring of user behavior, and analysis of each piece of information. The interest graph represents many kinds of relationships, including: between users and other users, users and items, and users and collections. The interest graph can be computed using data both from a set of items and from user behavior. | 09-18-2014 |
20140280121 | INTEREST GRAPH-POWERED FEED - Techniques for organizing information, such as documents, presentations, web sites and web pages, audiovisual media streams, and the like are describe. This disclosed techniques include creating and using an interest graph to support the distribution of information to a user via a feed containing information items likely to be of interest to the user. An interest graph expresses the affinity between people and information—the likelihood that a particular piece of information is of interest to a particular person. The interest graph is based on an understanding of relationships, monitoring of user behavior, and analysis of each piece of information. The interest graph represents many kinds of relationships, including: between users and other users, users and items, and users and collections. The interest graph can be computed using data both from a set of items and from user behavior. | 09-18-2014 |
Patent application number | Description | Published |
20080226174 | Image Organization - A system for organizing images includes an extraction component that extracts visual information (e.g., faces, scenes, etc.) from the images. The extracted visual information is provided to a comparison component which computes similarity confidence data between the extracted visual information. The similarity confidence data is an indication of the likelihood that items of extracted visual information are similar. The comparison component then generates a visual distribution of the extracted visual information based upon the similarity confidence data. The visual distribution can include groupings of the extracted visual information based on computed similarity confidence data. For example, the visual distribution can be a two-dimensional layout of faces organized based on the computed similarity confidence data—with faces in closer proximity faces computed to have a greater probability of representing the same person. The visual distribution can then be utilized by a user to sort, organize and/or tag images. | 09-18-2008 |
20080256007 | Learning A* priority function from unlabeled data - A technique for increasing efficiency of inference of structure variables (e.g., an inference problem) using a priority-driven algorithm rather than conventional dynamic programming. The technique employs a probable approximate underestimate which can be used to compute a probable approximate solution to the inference problem when used as a priority function (“a probable approximate underestimate function”) for a more computationally complex classification function. The probable approximate underestimate function can have a functional form of a simpler, easier to decode model. The model can be learned from unlabeled data by solving a linear/quadratic optimization problem. The priority function can be computed quickly, and can result in solutions that are substantially optimal. Using the priority function, computation efficiency of a classification function (e.g., discriminative classifier) can be increased using a generalization of the A* algorithm. | 10-16-2008 |
20080310687 | Face Recognition Using Discriminatively Trained Orthogonal Tensor Projections - Systems and methods are described for face recognition using discriminatively trained orthogonal rank one tensor projections. In an exemplary system, images are treated as tensors, rather than as conventional vectors of pixels. During runtime, the system designs visual features—embodied as tensor projections—that minimize intraclass differences between instances of the same face while maximizing interclass differences between the face and faces of different people. Tensor projections are pursued sequentially over a training set of images and take the form of a rank one tensor, i.e., the outer product of a set of vectors. An exemplary technique ensures that the tensor projections are orthogonal to one another, thereby increasing ability to generalize and discriminate image features over conventional techniques. Orthogonality among tensor projections is maintained by iteratively solving an ortho-constrained eigenvalue problem in one dimension of a tensor while solving unconstrained eigenvalue problems in additional dimensions of the tensor. | 12-18-2008 |
20120124061 | Rich Search Over and Deep Integration with Applications - An application search system may maintain an index of applications available from multiple different application stores, and includes parameters, such as features and/or content of the applications. When a user submits a query, the system may derive contextual information pertaining to a user device used to submit the query, applications installed on a particular user device and/or usage information for installed applications. The system then may, in one example, determine one or more applications relevant to the search query and, depending on the contextual information derived, may provide an entry point to access a particular application at a task level, may prompt the user to install the application, or may provide a web result related to the particular application. | 05-17-2012 |
20120124062 | Application Transfer Protocol - An application transfer protocol allows users to find applications relevant to a search query in an application search system. The application transfer protocol is used with an index that maintains a database of applications that includes parameters, such as features and/or content of the application. When a user submits a query, the system determines one or more applications relevant to the search query and implements the application transfer protocol to identify and present results to a user. | 05-17-2012 |
20120265433 | SUGGESTIVE MAPPING - A suggestive mapping device may predict, suggest, and/or provide potential destinations to a user. Additionally, the device may store historical location data of the user, determine a travel vector of the user, and predict the destination of the user based at least in part on the historical location data and/or the travel vector. Further, the device may provide hands-free maps to destinations when the user does not know the address at least by receiving contextual data of the user and/or contextual data of the user's contacts. Such hands-free, suggestive mapping devices may facilitate more effective navigation. | 10-18-2012 |
Patent application number | Description | Published |
20080320140 | CREDIT-BASED PEER-TO-PEER STORAGE - Distributed computing devices comprising a system for sharing computing resources can provide shared computing resources to users having sufficient resource credits. A user can earn resource credits by reliably offering a computing resource for sharing for a predetermined amount of time. The conversion rate between the amount of credits awarded, and the computing resources provided by a user can be varied to maintain balance within the system, and to foster beneficial user behavior. Once earned, the credits can be used to fund the user's account, joint accounts which include the user and others, or others' accounts that do not provide any access to the user. Computing resources can be exchanged on a peer-to-peer basis, though a centralized mechanism can link relevant peers together. To verify integrity, and protect against maliciousness, offered resources can be periodically tested. | 12-25-2008 |
20120158738 | Inference Indexing - Methods, systems, and media are provided for facilitating generation of an inference index. In embodiments, a canonical entity is referenced. The canonical entity is associated with web documents. One or more queries that, when input, result in a selection of at least one of the web documents are identified. An entity document is generated for the canonical entity. The entity document includes the identified queries and/or associated text from the content of a document or from an entity title that result in the selection of the at least one of the web documents. The entity document and corresponding canonical entity can be combined with additional related entity documents and canonical entities to generate an inference index. | 06-21-2012 |
20120284247 | INTEGRATING APPLICATIONS WITHIN SEARCH RESULTS - Computer-readable media, computer systems, and computing methods are provided for implicitly discovering applications using a query or search results, where the query does not explicitly target a particular application. A search engine is used to receive a user-initiated query and to employ a keyword of the query to generate an initial set of search results. Characteristics of the search results are identified and applications that are relevant to the characteristics are determined. The determination of relevant applications include: accessing an application manifest that manages a mapping between applications and predefined characteristics; comparing the characteristics of the search results against the predefined characteristics of the application manifest to determine a match; and, when a match exists between the search-result characteristics and the predefined characteristics, identifying applications mapped to the matched characteristics as being relevant to the search results. These identified applications are presented in response to the query. | 11-08-2012 |