Abstract: A hierarchy of clusters is determined, where each leave of the hierarchy corresponds to one of the images in a group, and each cluster in the hierarchy identifies images in the group that are deemed similar to one another. The hierarchy identifies a similarity between each of the plurality of clusters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting training images. One of the methods includes determining, for each of a plurality of labels that each designate a respective food class of a plurality of food classes, a respective measure of importance. A respective sample size is determined for the label based on the respective measure of importance of the label. A number of training images are selected for each respective label according to the determined sample size for the label. A predictive model is trained using the selected training images as training data.

Abstract: Implementations relate to techniques for classifying images. Some techniques utilize weights associated with local descriptors to classify images. Some techniques utilize visual phrase matching to classify images. The resulting image classifications can be used in part to assist in internet searches.

Abstract: Methods and systems for selecting a representative image of an entity are disclosed. According to one embodiment, a computer-implemented method for selecting a representative image of an entity is disclosed. The method includes: accessing a collection of images of the entity; clustering, based on similarity of one or more similarity features, images from the collection to form a plurality of similarity clusters; and selecting the representative image from one of said similarity clusters. Further, based on cluster size of said similarity clusters popular clusters can be determined, and the selection of the representative image can be from the popular clusters. In addition, the method can further include assigning a headshot score based upon a portion of the respective image covered by the entity to respective images in said popular clusters, and further selecting the representative image based upon the headshot score.

Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics.

Abstract: Methods and systems for selecting a representative image of an entity are disclosed. The representative image can be, but is not limited to, that of a person, a corporate logo, other symbol associated with a person or corporation. According to one embodiment, a computer-implemented method for selecting a representative image of an entity is disclosed. The method includes: accessing a collection of images of the entity; clustering, based on similarity of one or more similarity features, images from the collection to form a plurality of similarity clusters; and selecting the representative image from one of said similarity clusters. Further, based on cluster size of said similarity clusters popular clusters can be determined, and the selection of the representative image can be from the popular clusters.

Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics.

Abstract: Methods and systems for automated identification of celebrity face images are provided that generate a name list of prominent celebrities, obtain a set of images and corresponding feature vectors for each name, detect faces within the set of images, and remove non-face images. An analysis of the images is performed using an intra-model analysis, an inter-model analysis, and a spectral analysis to return highly accurate biometric models for each of the individuals present in the name list. Recognition is then performed based on precision and recall to identify the face images as belonging to a celebrity or indicate that the face is unknown.

Abstract: Methods and systems for automated identification of celebrity face images are provided that generate a name list of prominent celebrities, obtain a set of images and corresponding feature vectors for each name, detect faces within the set of images, and remove non-face images. An analysis of the images is performed using an intra-model analysis, an inter-model analysis, and a spectral analysis to return highly accurate biometric models for each of the individuals present in the name list. Recognition is then performed based on precision and recall to identify the face images as belonging to a celebrity or indicate that the face is unknown.

Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics.

Abstract: In a method and system and machine-readable medium, colors in an image (200) are unmixed (202) using a matrix X=AS, where A is a ns×nd matrix of spectral definitions, where s is the number of spectral components and d is the number of dyes into which the image is decomposed, and where S is a ns×l matrix of amounts of each dye at every pixel, where l is the number of pixels; the matrix X is constrained for solution by an unsupervised matrix decomposition method having constraints consistent with properties of an additive color model; and nd is determined. A texture is determined to identify areas of interest. Further processing may automatically measure cell dye concentration in the determined regions of interest.