Weighted Substructure Mining for Image Analysis

Nowozin, Sebastian; Tsuda, Koji; Uno, Takeaki; Kudo, Takeo; BakIr, Gökhan

doi:10.1109/cvpr.2007.383171

Cited by 77 publications

(95 citation statements)

References 18 publications

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“…In [18], these nodes are weighted and are discretised during preprocessing. Corresponding techniques are applied to graphs in transportation networks [19] and image analysis [20]. Such discretisation leads to a loss of information, as we will discuss in Sect.…”

Section: Subsumptionmentioning

confidence: 99%

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

Machine Learning and Knowledge Discovery in Databases

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An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.

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Section: Subsumptionmentioning

confidence: 99%

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

Machine Learning and Knowledge Discovery in Databases

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show abstract

“…In [3], Nowozin et al demonstrate good classification results with a method based on a combination of graph mining and boosting. The authors suggest that a representation of spatial relations between features is powerful compared to bag-of-words representations, and note that it has the important advantage of easier human interpretation.…”

Section: Related Workmentioning

confidence: 99%

Querying 3D Data by Adjacency Graphs

Bore

Folkesson

2015

Lecture Notes in Computer Science

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Abstract. The need for robots to search the 3D data they have saved is becoming more apparent. We present an approach for finding structures in 3D models such as those built by robots of their environment. The method extracts geometric primitives from point cloud data. An attributed graph over these primitives forms our representation of the surface structures. Recurring substructures are found with frequent graph mining techniques. We investigate if a model invariant to changes in size and reflection using only the geometric information of and between primitives can be discriminative enough for practical use. Experiments confirm that it can be used to support queries of 3D models.

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“…Frequent pattern mining techniques have been used in computer vision problems, including image classification [2,13,14], object recognition and object-part recognition [12]. These methods used different image representation, the way they convert image representation into transactional description which is suitable for pattern mining techniques and selects relevant and discriminative patterns.…”

Section: Related Workmentioning

confidence: 99%