Sentence examples for image graph from inspiring English sources

MMSF works on the obtained regions of pre-segmentation step that are considered as nodes of an image graph.

Then, the accuracy of selecting image pairs is obtained by comparing the selected image pairs to the ground truth image graph.

Then, an iterative re-ranking method is proposed to re-estimate the probabilities of relevance with the support of the image graph, and the final ranks of the images are obtained according to the values of probability values.

In the off-line part, image representations of both BOW and VLAD are generated, and we further use BOW to construct an image graph, which is a structure containing relationships between the dataset images.

For a given image graph G=〈V,E,w〉, a path π a =〈t 1,t 2,…,t n =a〉 is a sequence of adjacent pixels with terminus at a pixel a.

More importantly, this finding can also have implications when teaching science as argument (e.g., Erduran and Jimenez-Aleixandre 2008), since the verbal evidence used in the science classroom to maintain an argument could be supported by visual evidence (either a model, representation, image, graph, etc).

In MatchMiner, the ground truth image graphs are computed by exhaustive geometric verification on all image pairs.

Section 2 explains the basic concepts on image graphs and introduces the terminology and notation to be used throughout the text.

For message transferring among images, we construct an inter-image graph G Z == <Z,Y Z >>.

Then we build an intra-image graph G S  = <S,Y S  >, where S = {s i } is the superpixel set and Y S  = {(s i,s j )} is the edge set connecting all pairs of adjacent superpixels.

Yu et al. [19] created a bioscience image taxonomy (consisting of Gel-Image, Graph, Image-of-Thing, Mix, Model, and Table) and used Support Vector Machines to classify the figures, using properties of both the textual captions and the images.