Patent application number | Description | Published |
20090154795 | INTERACTIVE CONCEPT LEARNING IN IMAGE SEARCH - An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules. | 06-18-2009 |
20100241596 | INTERACTIVE VISUALIZATION FOR GENERATING ENSEMBLE CLASSIFIERS - A real-time visual feedback ensemble classifier generator and method for interactively generating an optimal ensemble classifier using a user interface. Embodiments of the real-time visual feedback ensemble classifier generator and method use a weight adjustment operation and a partitioning operation in the interactive generation process. In addition, the generator and method include a user interface that provides real-time visual feedback to a user so that the user can see how the weight adjustment and partitioning operation affect the overall accuracy of the ensemble classifier. Using the user interface and the interactive controls available on the user interface, a user can iteratively use one or both of the weigh adjustment operation and partitioning operation to generate an optimized ensemble classifier. | 09-23-2010 |
20100310134 | ASSISTED FACE RECOGNITION TAGGING - The described implementations relate to assisted face recognition tagging of digital images, and specifically to context-driven assisted face recognition tagging. In one case, context-driven assisted face recognition tagging (CDAFRT) tools can access face images associated with a photo gallery. The CDAFRT tools can perform context-driven face recognition to identify individual face images at a specified probability. In such a configuration, the probability that the individual face images are correctly identified can be higher than attempting to identify individual face images in isolation. | 12-09-2010 |
20100332423 | GENERALIZED ACTIVE LEARNING - Active learning is extended to decisions on information acquisition of both missing labels and missing features within one or more cases. In one example, desired (e.g., optimal) information to acquire about a case at hand and about cases in a training library during diagnostic sessions can be computed concurrently. A joint distribution of variables, comprising observed and unobserved labels and features for one or more cases, is modeled and probability distributions are determined for unobserved variables. An unobserved variable is selected from the joint distribution that has a return on information (ROI) metric having a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable. The value of the variable is observed, and the probability distributions for the respective unobserved variables in the joint distribution are updated using the value of the identified variable. | 12-30-2010 |
20110191271 | IMAGE TAGGING BASED UPON CROSS DOMAIN CONTEXT - A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships. | 08-04-2011 |
20110251980 | Interactive Optimization of the Behavior of a System - An interactive tool is described for modifying the behavior of a system, such as, but not limited to, the behavior of a classification system. The tool uses an interface mechanism to present a current global state of the system. The tool accepts one or more refinements to this global state, e.g., by accepting individual changes to parameter settings that are presented by the interface mechanism. Based on this input, the tool computes and displays the global implications of the updated parameter settings. The process of iterating over one or more cycles of user updates, followed by computation and display of the implications of the attempted refinements, has the effect of advancing the system towards a global state that exhibits desirable behavior. | 10-13-2011 |
20110292162 | NON-LINGUISTIC SIGNAL DETECTION AND FEEDBACK - Non-linguistic signal information relating to one or more participants to an interaction may be determined using communication data received from the one or more participants. Feedback can be provided based on the determined non-linguistic signals. The participants may be given an opportunity to opt in to having their non-linguistic signal information collected, and may be provided complete control over how their information is shared or used. | 12-01-2011 |
20110305392 | Resource-Aware Computer Vision - The described implementations relate to computer vision. In one case image data is received. Resource constraints associated with processing the image data are evaluated. Further, a visual recognition and detection processing strategy is selected for the image data based at least in part on the evaluated resource constraints. | 12-15-2011 |
20110307422 | EXPLORING DATA USING MULTIPLE MACHINE-LEARNING MODELS - A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data. | 12-15-2011 |
20120155759 | ESTABLISHING CLUSTERS OF USER PREFERENCES FOR IMAGE ENHANCEMENT - An image enhancement system may match images to a matrix having various enhancements of images for groups of users. The matrix may define image enhancement settings for the particular images and groups of users, and the matching may apply enhancements to a new image that closely matches a user's preferences. After the matrix is initially populated, new users and new images may be added to increase the matrix's accuracy. The image enhancement system may be deployed as a cloud service, where images may be enhanced as a standalone application or as part of a social network or image sharing website. In some embodiments, the image enhancement system may be deployed on a personal computer or as a component of an image capture device. | 06-21-2012 |
20120155765 | IMAGE QUALITY ASSESSMENT - Methods and systems for image quality assessment are disclosed. A method includes accessing an image, identifying features of the image, assessing the features and generating subjective scores for the features based upon a mapping of the features to the subjective scores and based on the subjective scores, generating an image quality score. Access is provided to the image quality score. | 06-21-2012 |
20120183206 | INTERACTIVE CONCEPT LEARNING IN IMAGE SEARCH - An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules. | 07-19-2012 |
20120239596 | CLASSIFICATION OF STREAM-BASED DATA USING MACHINE LEARNING - The described implementations relate to data classification. One implementation includes identifying one or more likely classifications for an incoming data item using an algorithm. The implementation can also include providing the one or more identified classifications to a user. A selection of an individual identified classification for the incoming data item can be received from the user. The algorithm can be refined to reflect the selection by the user. | 09-20-2012 |
20130204535 | VISUALIZING PREDICTED AFFECTIVE STATES OVER TIME - Described herein are various technologies pertaining to estimating affective states of a user by way of monitoring data streams output by sensors and user activity on a computing device. Models of valence, arousal, and engagement can be learned during a training phase, and such models can be employed to compute values that are indicative of valence, arousal, and engagement of a user in near-real time. A visualization that represents estimated affective states of a user over time is generated to facilitate user reflection. | 08-08-2013 |
20130294698 | IMAGE QUALITY ASSESSMENT - Methods and systems for image quality assessment are disclosed. A method includes accessing an image, identifying features of the image, assessing the features and generating subjective scores for the features based upon a mapping of the features to the subjective scores and based on the subjective scores, generating an image quality score. Access is provided to the image quality score. | 11-07-2013 |
20140129489 | IMAGE TAGGING BASED UPON CROSS DOMAIN CONTEXT - A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships. | 05-08-2014 |
20140282178 | PERSONALIZED COMMUNITY MODEL FOR SURFACING COMMANDS WITHIN PRODUCTIVITY APPLICATION USER INTERFACES - Systems and techniques for facilitating and backing the surfacing of predicted commands within a user interface are disclosed. Commands to surface for an active user in productivity applications can be predicted using a personalized community model. The personalized community model is generated using a record of past actions the active user has taken along with the past actions of many users of the productivity application. The actions of the active user within the productivity application are monitored and used to select commands to surface. | 09-18-2014 |