Four metrics for efficiently comparing attributed trees

Torsello, Andrea; Hidovic, D.; Pelillo, Marcello

doi:10.1109/icpr.2004.1334263

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…This method uses the size of MCS between two trees, or metrics defined by MCS as the similarity measure. In (Torsello, Hidovic & Pelillo 2004), four novel distance measures for attributed trees based on the notion of a maximum similarity sub-tree isomorphism were proposed. In (Lin et al 2008), the number of all common embedded sub-trees between two trees was used as the measure of similarity.…”

Section: Tree Similarity Measure Methodsmentioning

confidence: 99%

Tree Similarity Measure-based Recommender Systems

Wu¹

2020

Recommender Systems

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The rapid growth of web information provides excellent opportunities for developing e-services in many applications but also caused increasingly severe information overload problems whereby users are not able to locate relevant information to exactly meet their needs efficiently by using the current Internet search functions. A personalised recommender system aims to handle this issue.A big challenge in current recommender system research is: the items and user profiles in many recommender system applications nowadays, such as the e-business and e-learning recommender systems, are so complex that they can only be described in complicated tree structures. Therefore, the item or user similarity measure, as the core technique of the recommendation approach, becomes a tree similarity measure, which existing recommender systems cannot provide. Another challenge is that in many real life situations, online recommendations to customers in selecting the most suitable products/services are often made under incomplete and uncertain information, which needs fuzzy set theory and techniques to deal with. Thus, how to use fuzzy set techniques to handle data uncertainty issues in tree-structured items or user profiles needs to be investigated.This research aims to handle these two challenges in both theoretical and practical aspects. It first defines a tree-structured data model, which can be used to model treestructured items, user profiles and user preferences. A comprehensive similarity measure on tree-structured data considering all the information on tree structures, nodes' concepts, weights and values is then developed, which can be used to compute the semantic similarity between tree-structured items or users, and the matching degree of items to tree-structured user requests. Based on the tree-structured data model, the tree-structured iv items and user requirements are modelled as item trees and user request trees respectively. An item tree and user request tree-based hybrid recommendation approach is then developed. To model users' fuzzy tree-structured preferences, a fuzzy preference tree model is proposed. A fuzzy preference tree-based recommendation approach is then developed. Experimental results on an Australian business dataset and the Movielens dataset show that the proposed recommendation approaches have good performance and are well-suited in dealing with tree-structured data in recommender systems. By use of the proposed tree similarity measure and recommendation approaches based on that, two real world applications, a business partner recommender system, Smart BizSeeker, and an e-learning recommender system, ELRS, are designed and implemented, which demonstrate the applicability and effectiveness of the proposed approaches. v

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Section: Tree Similarity Measure Methodsmentioning

confidence: 99%

Tree Similarity Measure-based Recommender Systems

Wu¹

2020

Recommender Systems

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“…Several tree estimation algorithms, like the minimum editing distance 23 or four metrics, 24 which are quite effective and can be used to determine whether two objects are different or not. However, each node in those binary trees, illustrated in previous subsection, is a global statistical value.…”

Section: Hu's Moment Invariants Of Layersmentioning

confidence: 99%

A Line-Context Based Object Recognition Method

Wei

2014

Int. J. Artif. Intell. Tools

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The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be handled. The first is obtaining clean edges and the other is organizing those edges into a structured form so that they can be manipulated easily. We apply a bio-inspired orientation detection algorithm because it can output a fairly clean set of lines, and all lines are in the form of vectors instead of pixels. This line representation is efficient. We decompose them into several slope-depended layers and then create a hierarchical partition tree to record their geometric distribution. Based on the similarity of trees, a rough classification of objects can be realized. However, for an accuracy recognition, we design a momentbased measure to describe the detail layout of lines in a layer and then re-describe image by Hu's moment invariants. The experimental results suggest that the representation efficiency enabled by simple cell's neural mechanism and application of multi-layered representation schema can simplify the complexity of the algorithm. This proves that line-context representation greatly eases subsequent shape-oriented recognition.

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“…In order to avoid this size bias effect, we weight W (ϕ 12 ) relative to the original tree sizes. For this purpose we apply the four metrics proposed in [23], with |V i | the number of vertices in V i :…”

Section: Introductionmentioning

confidence: 99%

On the Use of the Tree Structure of Depth Levels for Comparing 3D Object Views

Bracci

Hillenbrand

Márton

et al. 2017

Computer Analysis of Images and Patterns

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Today the simple availability of 3D sensory data, the evolution of 3D representations, and their application to object recognition and scene analysis tasks promise to improve autonomy and flexibility of robots in several domains. However, there has been little research into what can be gained through the explicit inclusion of the structural relations between parts of objects when quantifying similarity of their shape, and hence for shape-based object category recognition. We propose a Mathematical Morphology inspired hierarchical decomposition of 3D object views into peak components at evenly spaced depth levels, casting the 3D shape similarity problem to a tree of more elementary similarity problems. The matching of these trees of peak components is here compared to matching the individual components through optimal and greedy assignment in a simple feature space, trying to find the maximum-weight-maximal-match assignments. The matching thus achieved provides a metric of total shape similarity between object views. The three matching strategies are evaluated and compared through the category recognition accuracy on objects from a public set of 3D models. It turns out that all three methods yield similar accuracy on the simple features we used, while the greedy method is fastest.

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Four metrics for efficiently comparing attributed trees

Cited by 12 publications

References 12 publications

Tree Similarity Measure-based Recommender Systems

Tree Similarity Measure-based Recommender Systems

A Line-Context Based Object Recognition Method

On the Use of the Tree Structure of Depth Levels for Comparing 3D Object Views

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