A system comprises an interface configured to receive an identifier, a processor configured to determine a grouping associated with the identifier, wherein the grouping is determined using a first clustering, wherein the first clustering is based at least in part on a language processing system, determine a sub-grouping of the grouping associated with the identifier, wherein the sub-grouping is determined using a second clustering, determine a final identifier based at least in part on the identifier and the sub-grouping, determine a resource based at least in part on the final identifier, and store the final identifier associated with the resource, and a memory coupled to the processor and configured to provide the processor with instructions.

Various systems and methods for for clustering facial images in, for example, surveillance systems.

Disclosed is a subject recognizing apparatus and method. The method may include extracting feature points from a target image, respectively measuring movement information of each of a plurality of the extracted feature points, selectively grouping the extracted feature points into one or more groups based on the respectively measured movement information, determining a type of subject present in at least one group of the one or more groups based on at least a portion of the subject present in the at least one group, and recognizing a subject included in the target image based on the determined type of subject.

Techniques are disclosed for incremental agglomerative clustering of data, including but not limited to digital image data. Fewer than all of a plurality of existing digital image fingerprints are sampled from a first hierarchical data cluster of digital image fingerprints stored in a data storage device, the first hierarchical data cluster excluding a new digital image fingerprint. The new digital image fingerprint and the existing digital image fingerprints sampled from the first hierarchical data cluster are clustered to produce a second hierarchical data cluster of digital image fingerprints, the second hierarchical data cluster including the new digital image fingerprint. If a majority of the existing digital image fingerprints in the first hierarchical data cluster match the new digital image fingerprint, then the second hierarchical data cluster is mapped to the first hierarchical data cluster based on the determination.

The present disclosure provides various approaches for smart area monitoring suitable for parking garages or other areas. These approaches may include ROI-based occupancy detection to determine whether particular parking spots are occupied by leveraging image data from image sensors, such as cameras. These approaches may also include multi-sensor object tracking using multiple sensors that are distributed across an area that leverage both image data and spatial information regarding the area, to provide precise object tracking across the sensors. Further approaches relate to various architectures and configurations for smart area monitoring systems, as well as visualization and processing techniques. For example, as opposed to presenting video of an area captured by cameras, 3D renderings may be generated and played from metadata extracted from sensors around the area.

Method and apparatus for recommending at least one of a makeup palette or a hair coloration scheme are provided. The method comprises extracting a color set of at least one region of a digital image associated with a user (201); generating color recommendation information for at least one of a makeup palette or a hair coloration scheme for at least two other regions of the digital image associated with the user based on one or more inputs indicative of the extracted color set (202); and generating one or more instances of a virtual representation of a makeup palette recommendation or a hair coloration scheme recommendation based on the color recommendation information (203).

A method is provided for classifying objects. The method detects objects in one or more images. The method tags each object with multiple features. Each feature describes a specific object attribute and has a range of values to assist with a determination of an overall quality of the one or more images. The method specifies a set of training examples by classifying the overall quality of at least some of the objects as being of an acceptable quality or an unacceptable quality, based on a user's domain knowledge about an application program that takes the objects as inputs. The method constructs a plurality of first-level classifiers using the set of training examples. The method constructs a second-level classifier from outputs of the first-level automatic classifiers. The second-level classifier is for providing a classification for at least some of the objects of either the acceptable quality or the unacceptable quality.

A computer-implemented method includes: receiving, by a computer device, an electronic document having labels; predicting, by the computer device, a user will reject the labels; determining, by the computer device and in response to the determining the user will reject the labels, that a subset of labels of the labels violate association rules; marking, by the computer device, the subset of labels which violate the association rules for validation; prioritizing, by the computer device, the subset of labels which violate the association rules; and rendering, by the computer device, the subset of labels which violate the association rules in view of priority.

A method of characterizing cellular phenotypes includes receiving multi-parameter cellular and sub-cellular imaging data for a number of tissue samples from a number of patients or a number of multicellular in vitro models, performing cellular segmentation on the multi-parameter cellular and sub-cellular imaging data to create segmented multi-parameter cellular and sub-cellular imaging data, and performing recursive decomposition on the segmented multi-parameter cellular and subcellular imaging data to identify a plurality of computational phenotypes. The recursive decomposition includes a plurality of levels of decomposition with each level of decomposition including soft/probabilistic clustering and spatial regularization, and each cell in the segmented multi-parameter cellular and subcellular imaging data is probabilistically assigned to one or more of the plurality of computational phenotypes.

Techniques are disclosed for incremental agglomerative clustering of data, including but not limited to digital image data. Fewer than all of a plurality of existing digital image fingerprints are sampled from a first hierarchical data cluster of digital image fingerprints stored in a data storage device, the first hierarchical data cluster excluding a new digital image fingerprint. The new digital image fingerprint and the existing digital image fingerprints sampled from the first hierarchical data cluster are clustered to produce a second hierarchical data cluster of digital image fingerprints, the second hierarchical data cluster including the new digital image fingerprint. If a majority of the existing digital image fingerprints in the first hierarchical data cluster match the new digital image fingerprint, then the second hierarchical data cluster is mapped to the first hierarchical data cluster based on the determination.