Abstract: A determination is made as to whether to merge clusters of objects. Semantic information is input for at least one of the objects. A compactness of a candidate cluster to be formed when a first cluster and a second cluster are merged is evaluated. A cluster quality of the candidate cluster is evaluated, based on the semantic information. The first cluster and the second cluster are merged in a case that the compactness of the candidate cluster relative to a compactness of the first and second clusters exceeds a compactness threshold, and the cluster quality of the candidate cluster relative to a cluster quality of the first and second clusters exceeds a cluster quality threshold.

Abstract: Systems, devices, and methods for generating attribute scores obtain a plurality of object images; generate a respective first attribute score of a first attribute for each object image in the plurality of object images based on the object images; calculate a respective pairwise object-similarity measure for pairs of object images in the plurality of object images; and refine the first attribute score of an object image in the plurality of object images based at least in part on the attribute scores of other object images in the plurality of object images and on the object-similarity measures of the pairs of object images in the plurality of object images.

Abstract: A method and a system for nucleic acid melting analysis are provided. Specifically, the system includes a biochip having at least one sample containing nucleic acids. A thermal generating apparatus ramps the temperature of the at least one sample to cause dissociation of the nucleic acids. A raw melting curve reflecting dissociation of the nucleic acids is generated. To analyze the raw nucleic acid melting curve, a normalization method is selected to define a mathematical relationship between a normalized melting curve and the raw melting curve. A derivative of the normalized melting curve is calculated based upon the mathematical relationship and a derivative of the raw melting curve obtained prior to calculating the normalized melting curve. Accordingly, the derivative of the normalized melting curve is calculated without using the Savitsky-Golay (SG) filter. The elimination of an additional SG filter in the melting analysis substantially reduces computation time.

Abstract: Systems and methods for measuring similarity between a set of clusters and a set of object labels, wherein at least two of the object labels are related, receive a first set of clusters, wherein the first set of clusters was formed by clustering objects in a set of objects into clusters of the first set of clusters according to a clustering procedure; and calculate a similarity index between the first set of clusters and a set of object labels based at least in part on a relationship between two or more object labels in the set of object labels.

Abstract: Systems and methods for scoring similarity count a number of matching visual words between a query image and a candidate image, generate an image similarity score for the query image and the candidate image based at least in part on the number of matching visual words, and generate a normalized image similarity score based at least in part on the image similarity score and one or more of a complexity of the query image and a complexity of the candidate image.

Abstract: A system and a method for slug edge detection in a microchannel of a microfluidic device is provided. Specifically, the system comprises an image sensor in communication with the microchannel. The microchannel has at least two fluid slugs each of which has a marker of different color providing color gradient across the edge between the adjacent fluid slugs. An edge score function is generated for each channel segmentation dividing the microchannel into two segments at a specific location along the microchannel. The edge score function is proportional to a between class variance for intensity values associated with the two selected channel segments. The edge location is determined as the location along the channel defining one of the channel segmentations based at least in part on the edge score function.

Abstract: Retrieval of images of objects from a large-scale database of object images, based on a query image. The database may, for example, contain images of objects such as faces, vehicles, people and luggage. Semantic attributes such as doors or windows in the case of vehicles are used as high level semantic cues to determine identities of objects in the images. Salient visual characteristics of the images are labeled with attribute information, and a transformation is learned so as to transform the labeled visual characteristics into a discrimination vector that discriminates between the labels. A similarity metric is learned using the discrimination vectors, such that different images depicting the same object are determined to be close while those having different objects are determined to be far apart. Candidates are retrieved based on a query image, and a re-ranking step may be applied to improve results. Validation experiments are described.

Abstract: Systems, devices, and methods for generating an image representation obtain a set of low-level features from an image; generate a high-dimensional generative representation of the low-level features; generate a lower-dimensional representation of the low-level features based on the high-dimensional generative representation of the low-level features; generate classifier scores based on classifiers and on one or more of the high-dimensional generative representation and the lower-dimensional representation, wherein each classifier uses the one or more of the high-dimensional generative representation and the lower-dimensional representation as an input, and wherein each classifier is associated with a respective label; and generate a combined representation for the image based on the classifier scores and the lower-dimensional representation.

Abstract: Systems, devices, and methods for generating hierarchical subspace maps obtain a training set of images, wherein the images in the training set of images are each associated with at least one category in a plurality of categories; organize the images in the training set of images into a category hierarchy based on the training set of images and on the plurality of categories, wherein the category hierarchy identifies each of the categories in the plurality of categories as at least one of a parent category and child category; and generate a subspace map for each parent category based on images associated with respective child categories of the parent category, thereby generating a plurality of subspace maps.

Abstract: Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

Abstract: Systems, devices, and methods for creating a visual vocabulary extract a plurality of descriptors from one or more labeled images; cluster the descriptors into augmented-space clusters in an augmented space, wherein the augmented space includes visual similarities and label similarities; generate a descriptor-space cluster in a descriptor space based on the augmented-space clusters, wherein one or more augmented-space clusters are associated with the descriptor-space cluster; and generate augmented-space classifiers for the augmented-space clusters that are associated with the descriptor-space cluster based on the augmented-space clusters.

Abstract: Systems and methods for learning a high-level visual vocabulary generate inter-visual-word relationships between a plurality of visual words based on visual word-label relationships, map the visual words to a vector space based on the inter-visual word relationships, and generate high-level visual words in the vector space.

Abstract: Systems and methods for generating visual words define initial inter-visual word relationships between a plurality of visual words; define visual word-image relationships between the plurality of visual words and a plurality of images; define inter-image relationships between the plurality of images; generate revised inter-visual word relationships in a vector space based on the initial inter-visual word relationships, the inter-image relationships, and the visual word-image relationships; and generate higher-level visual words in the vector space based on the revised inter-visual word relationships.

Abstract: Systems, devices, and methods for generating attribute scores obtain a plurality of object images; generate a respective first attribute score of a first attribute for each object image in the plurality of object images based on the object images; calculate a respective pairwise object-similarity measure for pairs of object images in the plurality of object images; and refine the first attribute score of an object image in the plurality of object images based at least in part on the attribute scores of other object images in the plurality of object images and on the object-similarity measures of the pairs of object images in the plurality of object images.

Abstract: Systems and methods for scoring similarity count a number of matching visual words between a query image and a candidate image, generate an image similarity score for the query image and the candidate image based at least in part on the number of matching visual words, and generate a normalized image similarity score based at least in part on the image similarity score and one or more of a complexity of the query image and a complexity of the candidate image.

Abstract: Systems and methods for organizing images extract low-level features from an image of a collection of images of a specified event, wherein the low-level features include visual characteristics calculated from the image pixel data, and wherein the specified event includes two or more sub-events; extract a high-level feature from the image, wherein the high-level feature includes characteristics calculated at least in part from one or more of the low-level features; identify a sub-events in the image based on the high-level feature and a predetermined model of the specified event, wherein the predetermined model describes a relationship between two or more sub-events; and annotate the image based on the identified sub-event.

Abstract: Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

Abstract: Systems and methods for clustering descriptors in a space of visual descriptors to generate augmented visual descriptors in an augmented space that includes semantic information, wherein the augmented space of the augmented descriptors includes both visual descriptor-to-descriptor dissimilarities and semantic label-to-label dissimilarities; and cluster the augmented visual descriptors in the augmented space based at least in part on a dissimilarity measure between augmented visual descriptors in the augmented descriptor space.

Abstract: Representative images are selected for display from a group. A dissimilarity measure is selected, by which to compute dissimilarities between features of respective images in the group. Dissimilarities between the images in the group are computed, based on the selected dissimilarity measure. A relative dissimilarity between each image and the other images in the group is determined, based on the relative dissimilarity between the feature of the image and the features of the other images in the group. An image in the group is selected as a representative image for display, using the relative dissimilarity of the image as a probability bias in the selection.

Abstract: Systems and methods for measuring similarity between a set of clusters and a set of object labels, wherein at least two of the object labels are related, receive a first set of clusters, wherein the first set of clusters was formed by clustering objects in a set of objects into clusters of the first set of clusters according to a clustering procedure; and calculate a similarity index between the first set of clusters and a set of object labels based at least in part on a relationship between two or more object labels in the set of object labels