Abstract: Systems and methods for measuring similarity between a first set of clusters and a second set of clusters apply a first clustering procedure and a second clustering procedure to a set of objects to cluster the objects into a first set of clusters and a second set of clusters, respectively, calculate a similarity index between the first set of clusters and the second set of clusters, calculate an expected value of the similarity index, wherein the expected value is a value of the similarity index one would expect to obtain, on average, between a randomly generated third set of clusters and a randomly generated fourth set of clusters with a same number of clusters as the first set of clusters and the second set of clusters, respectively, and adjust the calculated similarity index based on the expected value of the similarity index.

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: A clustering procedure for grouping a set of images is selected from amongst plural clustering procedures. A predetermined categorization of objects such as images is input, and image features are extracted from each image in the set of images. A comparison measure is determined, by which to compare respective features of the set of images. Respective features between the images in the set of images are compared, based on the comparison measure, and a group of measures representing the differences between features of respective images is output. The plural clustering procedures are applied to the set of images to cluster the images based in part on the calculated group of measures. A clustering quality score is generated for each clustering procedure, based on the clusters created by the clustering procedure and the predetermined categorization of images. The clustering procedure with a high clustering quality score is selected.

Abstract: Systems and methods for measuring similarity between a first set of clusters and a second set of clusters apply a first clustering procedure and a second clustering procedure to a set of objects to cluster the objects into a first set of clusters and a second set of clusters, respectively, calculate a similarity index between the first set of clusters and the second set of clusters, calculate an expected value of the similarity index, wherein the expected value is a value of the similarity index one would expect to obtain, on average, between a randomly generated third set of clusters and a randomly generated fourth set of clusters with a same number of clusters as the first set of clusters and the second set of clusters, respectively, and adjust the calculated similarity index based on the expected value of the similarity index.

Abstract: A seismic vibrator has a baseplate composed at least partially of a composite material. The baseplate has a body composed of the composite material and has top and bottom plates composed of a metallic material. The top plate supports isolators for isolating the vibrator's mass and frame from the baseplate. Internally, the composite body has a central structure to which couple stilts for supporting the mass and a piston for the vibrator's actuator. A lattice structure surrounds the central structure. This lattice structure has radial ribs extending from the central structure and has radial ribs interconnecting the radial ribs.

Abstract: A clustering procedure for grouping a set of images is selected from amongst plural clustering procedures. A predetermined categorization of objects such as images is input, and image features are extracted from each image in the set of images. A comparison measure is determined, by which to compare respective features of the set of images. Respective features between the images in the set of images are compared, based on the comparison measure, and a group of measures representing the differences between features of respective images is output. The plural clustering procedures are applied to the set of images to cluster the images based in part on the calculated group of measures. A clustering quality score is generated for each clustering procedure, based on the clusters created by the clustering procedure and the predetermined categorization of images. The clustering procedure with a high clustering quality score is selected.

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.