Topic
Adjacency list
About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.
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08 Oct 2016
TL;DR: The MCS based algorithm allows multiple, similar objects to be co-segmented and the region co-growing stage helps to extract different sized, similar items from an image pair or a set of images.
Abstract: We propose a computationally efficient graph based image co-segmentation algorithm where we extract objects with similar features from an image pair or a set of images. First we build a region adjacency graph (RAG) for each image by representing image superpixels as nodes. Then we compute the maximum common subgraph (MCS) between the RAGs using the minimum vertex cover of a product graph obtained from the RAG. Next using MCS outputs as the seeds, we iteratively co-grow the matched regions obtained from the MCS in each of the constituent images by using a weighted measure of inter-image feature similarities among the already matched regions and their neighbors that have not been matched yet. Upon convergence, we obtain the co-segmented objects. The MCS based algorithm allows multiple, similar objects to be co-segmented and the region co-growing stage helps to extract different sized, similar objects. Superiority of the proposed method is demonstrated by processing images containing different sized objects and multiple objects.
21 citations
TL;DR: In this paper, a complete description of the adjacency relations between the l-modular singularities is given. But this is not a complete analysis of the relation between the singularities.
Abstract: We give a complete description of the adjacency relations between the l-modular singularities.
21 citations
02 Jul 2012
TL;DR: A new method is presented for hyperspectral band selection problem where the nodes represent the bands and the edges represent the similarity weights between the bands to create a band adjacency graph (BAG).
Abstract: In this paper we present a new method for hyperspectral band selection problem. The principle is to create a band adjacency graph (BAG) where the nodes represent the bands and the edges represent the similarity weights between the bands. The Markov Clustering Process (abbreviated MCL process) defines a sequence of stochastic matrices by alternation of two operators on the associated affinity matrix to form distinct clusters of high correlated bands. Each cluster is represented by one band and the representative bands will form the new data cube to be used in subsequent processing. The proposed algorithm is tested on a real dataset and compared against state-of-art. The results are promising.
20 citations
Book Chapter•
01 Mar 2010
TL;DR: In this article, the definability of finite graphs in first-order logic with two relation symbols for adjacency and equality of vertices is discussed, and the authors survey known estimates for these graph parameters and dis-cuss their relations to other topics.
Abstract: We discuss the definability of finite graphs in first-order logic with two relation symbols for adjacency and equality of vertices. The logical depth D(G) of a graph G is equal to the minimum quantifier depth of a sentence defining G up to isomorphism. The logical width W(G) is the minimum number of variables occurring in such a sentence. The logical length L(G) is the length of a shortest defining sentence. We survey known estimates for these graph parameters and dis- cuss their relations to other topics (such as the efficiency of the Weisfeiler-Lehman algorithm in isomorphism testing, the evolution of a random graph, quantitative characteristics of the zero-one law, or the contribution of Frank Ramsey to the research on Hilbert's Entscheidungsproblem). Also, we trace the behavior of the descriptive complexity of a graph as the logic becomes more restrictive (for exam- ple, only definitions with a bounded number of variables or quantifier alternations are allowed) or more expressible (after powering with counting quantifiers).
20 citations
Patent•
25 Apr 2005
TL;DR: In this paper, a data structure for a graph composed by points and line segments connecting adjacent pairs of the points, respectively, of a two- or three-dimensional object comprises a set of point data of the individual points, each of which consists of a clockwise or counter clockwise circular ordered adjacency list written in storage areas of a storage medium individually allocated to the point.
Abstract: A method for two- or three-dimensional object modelling is disclosed. A data structure for a graph, which is composed by points and line segments connecting adjacent pairs of the points, respectively, of a two- or three-dimensional object comprises a set of point data of the individual points, each of the point data of the points consists of a clockwise or counter clockwise circular ordered adjacency list written in storage areas of a storage medium individually allocated to the point. The adjacency list describes a plurality of adjacent points associated with the point sequentially according to a circular adjacency order as viewed from outside of the graph with address pointers to the storage areas allocated to the adjacent points, respectively, and location pointers associated with the address pointers, respectively, to storage locations of adjacency lists of the adjacent points, respectively, where an address pointer of the point is written.
20 citations