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Results 1 - 10
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633
Distortion invariant object recognition in the dynamic link architecture
- IEEE TRANSACTIONS ON COMPUTERS
, 1993
"... We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture ex-ploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into hig ..."
Abstract
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Cited by 637 (80 self)
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We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture ex-ploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically
SimRank: A Measure of Structural-Context Similarity
- In KDD
, 2002
"... The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to- ..."
Abstract
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Cited by 387 (3 self)
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The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object
Path similarity skeleton graph matching
- IEEE TRANS. PAMI
, 2008
"... This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the to ..."
Abstract
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Cited by 53 (8 self)
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the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned
Clustering of video objects by graph matching
- In Proc. of IEEE International Conference on Multimedia and Expo (ICME
, 2005
"... ABSTRACT We propose a new graph-based data structure, called Spatio Temporal Region Graph (STRG) which can represent the content of video sequence. Unlike existing ones which consider mainly spatial information in the frame level of video, the proposed STRG is able to formulate its temporal informa ..."
Abstract
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Cited by 5 (0 self)
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information in the video level additionally. After an STRG is constructed from a given video sequence, it is decomposed into its subgraphs called Object Graphs (OGs), which represent the temporal characteristics of video objects. For unsupervised learning, we cluster similar OGs into a group, in which we need
Learning Graphs to Match
- ICCV 2013
, 2013
"... Many tasks in computer vision are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to significant progress in applications such as image matching, shape analysis, action recognition, and object recognition. However, learning ..."
Abstract
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Cited by 10 (2 self)
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graph models from observed data still remains a challenging problem. This paper presents an effective scheme to parameterize a graph model, and learn its structure and parameters for visual object category matching. For this, we propose a graph representation with histogram-based attributes
Learning to Rank Graphs for Online Similar Graph Search
"... Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chemoinformatics, have used the maximum common edge subgraph (MCEG) to compute the graph similarity. This approach is prohibi ..."
Abstract
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Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chemoinformatics, have used the maximum common edge subgraph (MCEG) to compute the graph similarity. This approach
On the Performance of Percolation Graph Matching
"... Graph matching is a generalization of the classic graph isomorphism problem. By using only their structures a graph-matching algorithm finds a map between the vertex sets of two similar graphs. This has applications in the deanonymization of social and information networks and, more generally, in th ..."
Abstract
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Cited by 10 (2 self)
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Graph matching is a generalization of the classic graph isomorphism problem. By using only their structures a graph-matching algorithm finds a map between the vertex sets of two similar graphs. This has applications in the deanonymization of social and information networks and, more generally
Learning Context-Sensitive Shape Similarity by Graph Transduction
, 2010
"... Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this probl ..."
Abstract
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Cited by 42 (7 self)
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to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape similarity measure. For a given similarity measure, a new similarity is learned through graph transduction
Learning mixed Kronecker product graph . . .
, 2013
"... There has recently been a great deal of work focused on developing statistical models of graph structure—with the goal of modeling probability distributions over graphs from which new, similar graphs can be generated by sampling from the estimated distributions. Although current graph models can cap ..."
Abstract
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There has recently been a great deal of work focused on developing statistical models of graph structure—with the goal of modeling probability distributions over graphs from which new, similar graphs can be generated by sampling from the estimated distributions. Although current graph models can
Graph matching: Theoretical foundations, algorithms, and applications
- In Proceedings of Vision Interface 2000, Montreal
, 2000
"... Graphs are a powerful and versatile tool useful in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects. When graphs are used for the representation of structured objects, then ..."
Abstract
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Cited by 51 (0 self)
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Graphs are a powerful and versatile tool useful in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects. When graphs are used for the representation of structured objects
Results 1 - 10
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633
