Multi-class Graph Boosting with Subgraph Sharing for Object Recognition

Zhang, Bang; Ye, Getian; Wang, Yang; Wang, Wei; Xu, Jie; Herman, Gunawan; Ye, Yun

doi:10.1109/icpr.2010.381

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…Nowozin et al developed a classifier based on LPBoost [34]. Zhang et al [3] improved classifiers by incorporating an error-correcting coding matrix method [35] into boosting. Both [34] and [3] adopted the decision stump as a weak classifier.…”

Section: Related Workmentioning

confidence: 99%

“…The difference between GMB and these methods [3], [33], [34] is the structural variation in the classifiers. Comprehensive structural variation is incorporated into the GMB, whereas local structural variation is used in [3], [33], [34] because of the subgraphs. The advantage of the embedding approach is that it can exploit powerful recognition methods.…”

Section: Related Workmentioning

confidence: 99%

“…where O is a function that refers to the position of w k in ascending order of W . For example, given W = (3,6,1,9), O(6) = 3. Let ⋄ represent a duplication operator, such as x ⋄ N = {x, x, · · · , x N times }.…”

Section: Model Extension For Weighted Training Datamentioning

confidence: 99%

“…Even measuring the distance between graphs requires various techniques. The problem of graph recognition has recently been actively studied [1], [2], [3], [4], [5]. Past research has led to notable progress in two aspects.…”

Section: Introductionmentioning

confidence: 99%

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Structural Data Recognition With Graph Model Boosting

Miyazaki

Omachi

2018

IEEE Access

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This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository. ✦ • T. Miyazaki and S. Omachi are with the

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Model Extension For Weighted Training Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural Data Recognition With Graph Model Boosting

Miyazaki

Omachi

2018

IEEE Access

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“…Moreover, Jin et al [40] proposes an efficient graph classification method using evolutionary computation for mining discriminative subgraphs for graph classification in large databases. Besides, some graph boosting methods [41], [42], [43], [44] also exist to use each single subgraph feature as a weak classifier to build boosting algorithm, including some other types of boosting approaches [45], [46] for graph classification.…”

Section: B Graph Classificationmentioning

confidence: 99%

Boosting for Multi-Graph Classification

Pan

Zhu

et al. 2015

IEEE Trans. Cybern.

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In this paper, we formulate a novel graph-based learning problem, multi-graph classification (MGC), which aims to learn a classifier from a set of labeled bags each containing a number of graphs inside the bag. A bag is labeled positive, if at least one graph in the bag is positive, and negative otherwise. Such a multi-graph representation can be used for many real-world applications, such as webpage classification, where a webpage can be regarded as a bag with texts and images inside the webpage being represented as graphs. This problem is a generalization of multi-instance learning (MIL) but with vital differences, mainly because instances in MIL share a common feature space whereas no feature is available to represent graphs in a multi-graph bag. To solve the problem, we propose a boosting based multi-graph classification framework (bMGC). Given a set of labeled multi-graph bags, bMGC employs dynamic weight adjustment at both bag- and graph-levels to select one subgraph in each iteration as a weak classifier. In each iteration, bag and graph weights are adjusted such that an incorrectly classified bag will receive a higher weight because its predicted bag label conflicts to the genuine label, whereas an incorrectly classified graph will receive a lower weight value if the graph is in a positive bag (or a higher weight if the graph is in a negative bag). Accordingly, bMGC is able to differentiate graphs in positive and negative bags to derive effective classifiers to form a boosting model for MGC. Experiments and comparisons on real-world multi-graph learning tasks demonstrate the algorithm performance.

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Stable Learning in Coding Space for Multi-class Decoding and Its Extension for Multi-class Hypothesis Transfer Learning

Zhang

Wang

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Multi-class Graph Boosting with Subgraph Sharing for Object Recognition

Cited by 6 publications

References 13 publications

Structural Data Recognition With Graph Model Boosting

Structural Data Recognition With Graph Model Boosting

Boosting for Multi-Graph Classification

Stable Learning in Coding Space for Multi-class Decoding and Its Extension for Multi-class Hypothesis Transfer Learning

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