Abstract:
A system and computer-implemented method is provided for organizing multiple user submitted results responsive to an image query. A plurality of content submissions may be received from a variety of submitting users, each content submission including an image and an associated label. An image query may provide an image of an object as a request to identify the object. In response to receiving the image query, one or more results of the plurality of content submissions may be identified. A similarity between the labels for each of the one or more results may be determined and used to group the one or more results. Grouped results may be ranked and sorted for accurate and concise presentation to a querying user.
Abstract:
A system and computer-implemented method is provided for organizing multiple user submitted results responsive to an image query. A plurality of content submissions may be received from a variety of submitting users, each content submission including an image and an associated label. An image query may provide an image of an object as a request to identify the object. In response to receiving the image query, one or more results of the plurality of content submissions may be identified. A similarity between the labels for each of the one or more results may be determined and used to group the one or more results. Grouped results may be ranked and sorted for accurate and concise presentation to a querying user.
Abstract:
Systems and methods for modeling the occurrence of common image components (e.g., sub-regions) in order to improve visual object recognition are disclosed. In one example, a query image may be matched to a training image of an object. A matched region within the training image to which the query image matches may be determined and a determination may be made whether the matched region is located within an annotated image component of the training image. When the matched region matches only to the image component, an annotation associated with the component may be identified. In another example, sub-regions within a plurality of training image corpora may be annotated as common image components including associated information (e.g., metadata). Matching sub-regions appearing in many training images of objects may be down-weighted in the matching process to reduce possible false matches to query images including common image components.
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatically extracting logos from images. Methods include generating a query list including a plurality of logo search queries, for each logo search query of the plurality of logo search queries: generating a plurality of image search results, each image search result including image data, and clustering the plurality of image search results into a plurality of clusters, each cluster including a plurality of images of the plurality of image search results, extracting, for each cluster of the plurality of clusters, a representative image to provide a plurality of representative images, and a name corresponding to the representative image to provide a plurality of names, and providing the plurality of representative images and the plurality of names to a logo index, the logo index being accessible to identify one or more logo images in a query image.
Abstract:
Systems and methods for modeling the occurrence of common image components (e.g., sub-regions) in order to improve visual object recognition are disclosed. In one example, a query image may be matched to a training image of an object. A matched region within the training image to which the query image matches may be determined and a determination may be made whether the matched region is located within an annotated image component of the training image. When the matched region matches only to the image component, an annotation associated with the component may be identified. In another example, sub-regions within a plurality of training image corpora may be annotated as common image components including associated information (e.g., metadata). Matching sub-regions appearing in many training images of objects may be down-weighted in the matching process to reduce possible false matches to query images including common image components.
Abstract:
Systems and methods for improving visual object recognition by analyzing query images are disclosed. In one example, a visual object recognition module may determine query images matching objects of a training corpus utilized by the module. Matched query images may be added to the training corpus as training images of a matched object to expand the recognition of the object by the module. In another example, relevant candidate image corpora from a pool of image data may be automatically selected by matching the candidate image corpora against user query images. Selected image corpora may be added to a training corpus to improve recognition coverage. In yet another example, objects unknown to a visual object recognition module may be discovered by clustering query images. Clusters of similar query images may be annotated and added into a training corpus to improve recognition coverage.
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatically extracting logos from images. Methods include generating a query list including a plurality of logo search queries, for each logo search query of the plurality of logo search queries: generating a plurality of image search results, each image search result including image data, and clustering the plurality of image search results into a plurality of clusters, each cluster including a plurality of images of the plurality of image search results, extracting, for each cluster of the plurality of clusters, a representative image to provide a plurality of representative images, and a name corresponding to the representative image to provide a plurality of names, and providing the plurality of representative images and the plurality of names to a logo index, the logo index being accessible to identify one or more logo images in a query image.
Abstract:
A system and computer-implemented method for associating images with semantic entities and providing search results using the semantic entities. An image database contains one or more source images associated with one or more images labels. A computer may generate one or more documents containing the labels associated with each image. Analysis may be performed on the one or more documents to associate the source images with semantic entities. The semantic entities may be used to provide search results. In response to receiving a target image as a search query, the target image may be compared with the source images to identify similar images. The semantic entities associated with the similar images may be used to determine a semantic entity for the target image. The semantic entity for the target image may be used to provide search results in response to the search initiated by the target image.
Abstract:
Methods, systems, and apparatus for scoring images related to entities. In one aspect, a method includes identifying images associated with a person, each image being included in one or more resources; obtaining, for each resource that includes one of the images, a quality score that represents a quality of the resource; for each of the images: generating an image resource quality score from the quality scores of the resources that include the image; identifying a set of similar images from the images, each similar image having a measure of similarity to the image that meets a similarity measure threshold; generating an image score based on image resource quality scores of the resources that include the similar images relative to image resource quality scores of the resources that include each of the images; and generating an image authority score based on the image resource quality score and the image score.
Abstract:
Methods, systems, and apparatus for scoring images related to entities. In one aspect, a method includes identifying images associated with a person, each image being included in one or more resources; obtaining, for each resource that includes one of the images, a quality score that represents a quality of the resource; for each of the images: generating an image resource quality score from the quality scores of the resources that include the image; identifying a set of similar images from the images, each similar image having a measure of similarity to the image that meets a similarity measure threshold; generating an image score based on image resource quality scores of the resources that include the similar images relative to image resource quality scores of the resources that include each of the images; and generating an image authority score based on the image resource quality score and the image score.