Patent application number | Description | Published |
20080247660 | Automatic Detection and Mapping of Symmetries in an Image - A method, system, and computer-readable storage medium are disclosed for determining one or more symmetries in an image comprising a plurality of pixels. A symmetry value may be automatically determined for each of the plurality of pixels. The symmetry value may indicate the strength of one or more symmetries in the image for the respective pixel. The symmetry value may be stored for each of the pixels. | 10-09-2008 |
20120076425 | Locating a Feature in a Digital Image - Methods, systems, and computer program products used to locate a feature in an image. | 03-29-2012 |
20130282712 | COMBINED SEMANTIC DESCRIPTION AND VISUAL ATTRIBUTE SEARCH - An image search method includes receiving a first query, the first query providing a first image constraint. A first search of a plurality of images is performed, responsive to the first query, to identify a first set of images satisfying the first constraint. A first search result, which includes the first set of images identified as satisfying the first constraint, is presented. A second query is received, the second query providing a second image constraint with reference to a first image of the first set of images. A second search of the plurality of images is performed, responsive to the second query, to identify a second set of images that satisfy the second constraint. A second search result, which includes the second set of images identified as satisfying the second constraint, is presented. | 10-24-2013 |
20140037195 | IMAGE TAG PAIR GRAPH FOR IMAGE ANNOTATION - An approach is described for automatically tagging a single image or multiple images. The approach, in one example embodiment, is based on a graph-based framework that exploits both visual similarity between images and tag correlation within individual images. The problem is formulated in the context of semi-supervised learning, where a graph modeled as a Gaussian Markov Random Field (MRF) is solved by minimizing an objective function (the image tag score function) using an iterative approach. The iterative approach, in one embodiment, comprises: (1) fixing tags and propagating image tag likelihood values from labeled images to unlabeled images, and (2) fixing images and propagating image tag likelihood based on tag correlation. | 02-06-2014 |
20140040262 | TECHNIQUES FOR CLOUD-BASED SIMILARITY SEARCHES - Techniques for facilitating a similarity search of digital assets (e.g., audio files, image files, video files, etc.) are described. Consistent with some embodiments, a cloud-based search service manages one or more search tree data structures for use in organizing digital assets to make the digital assets searchable. Each digital asset is associated with a feature vector based on the various attributes and/or characteristics of the digital asset. The digital assets are then assigned to leaf nodes in one or more search tree data structures based on a measure of the distance between the feature vector of the digital asset and a virtual feature vector associated with a leaf node. When a search for similar digital assets is invoked, a prioritized breadth first search of a search tree is performed to identify the digital assets having the feature vectors closest in distance to the reference digital asset. | 02-06-2014 |
20140153817 | Patch Size Adaptation for Image Enhancement - Systems and methods are provided for providing patch size adaptation for patch-based image enhancement operations. In one embodiment, an image manipulation application receives an input image. The image manipulation application compares a value for an attribute of at least one input patch of the input image to a threshold value. Based on comparing the value for the to the threshold value, the image manipulation application adjusts a first patch size of the input patch to a second patch size that improves performance of a patch-based image enhancement operation as compared to the first patch size. The image manipulation application performs the patch-based image enhancement operation based on one or more input patches of the input image having the second patch size. | 06-05-2014 |
20140153819 | Learned Piece-Wise Patch Regression for Image Enhancement - Systems and methods are provided for providing learned, piece-wise patch regression for image enhancement. In one embodiment, an image manipulation application generates training patch pairs that include training input patches and training output patches. Each training patch pair includes a respective training input patch from a training input image and a respective training output patch from a training output image. The training input image and the training output image include at least some of the same image content. The image manipulation application determines patch-pair functions from at least some of the training patch pairs. Each patch-pair function corresponds to a modification to a respective training input patch to generate a respective training output patch. The image manipulation application receives an input image generates an output image from the input image by applying at least some of the patch-pair functions based on at least some input patches of the input image. | 06-05-2014 |
20140247963 | OBJECT DETECTION VIA VALIDATION WITH VISUAL SEARCH - One exemplary embodiment involves receiving, at a computing device comprising a processor, a test image having a candidate object and a set of object images detected to depict a similar object as the test image. The embodiment involves localizing the object depicted in each one of the object images based on the candidate object in the test image to determine a location of the object in each respective object image and then generating a validation score for the candidate object in the test image based at least in part on the determined location of the object in the respective object image and known location of the object in the same respective object image. The embodiment also involves computing a final detection score for the candidate object based on the validation score that indicates a confidence level that the object in the test image is located as indicated by the candidate object. | 09-04-2014 |
20140247992 | ATTRIBUTE RECOGNITION VIA VISUAL SEARCH - One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a test image feature, and wherein the object depicted in the test image comprises a plurality of attributes. Additionally, the embodiment involves estimating, for each attribute in the test image, an attribute value based at least in part on information stored in a metadata associated with each of the object images. | 09-04-2014 |
20140247993 | LANDMARK LOCALIZATION VIA VISUAL SEARCH - One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each of the feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a first landmark of the object image, the first landmark at a known location in the object image. The embodiment additionally involves estimating a plurality of locations for a second landmark for the test image, the estimated locations based at least in part on the feature matches and the spatial relationships, and estimating a final location for the second landmark from the plurality of locations for the second landmark for the test image. | 09-04-2014 |
20140247996 | OBJECT DETECTION VIA VISUAL SEARCH - One exemplary embodiment involves receiving a test image generating, by a plurality of maps for the test image based on a plurality of object images. Each of the object images comprises an object of a same object type, e.g., each comprising a different face. Each of the plurality of maps is generated to provide information about the similarity of at least a portion of a respective object image to each of a plurality of portions of the test image. The exemplary embodiment further comprises detecting a test image object within the test image based at least in part on the plurality of maps. | 09-04-2014 |
20150030238 | VISUAL PATTERN RECOGNITION IN AN IMAGE - A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems. | 01-29-2015 |
20150063713 | GENERATING A HIERARCHY OF VISUAL PATTERN CLASSES - A hierarchy machine may be configured as a clustering machine that utilizes local feature embedding to organize visual patterns into nodes that each represent one or more visual patterns. These nodes may be arranged as a hierarchy in which a node may have a parent-child relationship with one or more other nodes. The hierarchy machine may implement a node splitting and tree-learning algorithm that includes hard-splitting of nodes and soft-assignment of nodes to perform error-bounded splitting of nodes into clusters. This may enable the hierarchy machine, which may form all or part of a visual pattern recognition system, to perform large-scale visual pattern recognition, such as font recognition or facial recognition, based on a learned error-bounded tree of visual patterns. | 03-05-2015 |