Computer Vision Pattern Recognition

Graph classification and clustering based on vector space by Riesen K., Bunke H.

By Riesen K., Bunke H.

Show description

Read or Download Graph classification and clustering based on vector space embedding PDF

Similar computer vision & pattern recognition books

Robot Motion Planning

One of many final targets in Robotics is to create self reliant robots. Such robots will settle for high-level descriptions of projects and may execute them with no extra human intervention. The enter descriptions will specify what the person wishes performed instead of the way to do it. The robots could be any type of flexible machine outfitted with actuators and sensors lower than the keep an eye on of a computing approach.

Advanced Technologies in Ad Hoc and Sensor Networks: Proceedings of the 7th China Conference on Wireless Sensor Networks

Complex applied sciences in advert Hoc and Sensor Networks collects chosen papers from the seventh China convention on instant Sensor Networks (CWSN2013) held in Qingdao, October 17-19, 2013. The e-book beneficial properties state of the art stories on Sensor Networks in China with the topic of “Advances in instant sensor networks of China”.

Advances in Biometrics for Secure Human Authentication and Recognition

''Supplying a high-level evaluation of the way to guard your company's actual and intangible resources, Asset defense via defense expertise explains the easiest how one can enlist the help of your staff because the first defensive line in safeguarding corporation resources and mitigating safety hazards. It experiences key issues surrounding computing device security--including privateness, entry controls, and hazard management--to assist you fill the gaps that would exist among administration and the technicians securing your community structures.

Additional resources for Graph classification and clustering based on vector space embedding

Sample text

In the former case, for a matching to be successful, it is required that a strict correspondence is found between the two graphs being matched, or at least among their subparts. In the latter approach this requirement is substantially relaxed, since also matchings between completely non-identical graphs are possible. That is, inexact matching algorithms are endowed with a certain tolerance to errors and noise, enabling them to detect similarities in a more general way than the exact matching approach.

Let g1 = (V1 , E1 , µ1 , ν1 ) and g2 = (V2 , E2 , µ2 , ν2 ) be graphs. A common supergraph December 28, 2009 26 9:59 Classification and Clustering clustering Graph Classification and Clustering Based on Vector Space Embedding of g1 and g2 , CS(g1 , g2 ), is a graph g = (V, E, µ, ν) such that there exist subgraph isomorphisms from g1 to g and from g2 to g. We call g a minimum common supergraph of g1 and g2 , M CS(g1 , g2 ), if there exists no other common supergraph of g1 and g2 that has less nodes than g.

In practice, however, a few weak conditions on the cost function c are sufficient so that December 28, 2009 40 9:59 Classification and Clustering clustering Graph Classification and Clustering Based on Vector Space Embedding only a finite number of edit paths have to be evaluated to find the minimum cost edit path among all valid paths between two given graphs. e. c(e) ≥ 0 , for all node and edge edit operations e. 1) We refer to this condition as non-negativity. Since the cost function assigns a penalty cost to a certain edit operation, this condition is certainly reasonable.

Download PDF sample

Rated 4.42 of 5 – based on 35 votes