New Approach for 3D Mesh Retrieval Using Data Envelopment Analysis

Bouksim, Mohcine; Zakani, Fatima Rafii; Arhid, Khadija; Mohamed, Azah; Gadi, Taoufiq

doi:10.22266/ijies2018.0228.01

Cited by 7 publications

(14 citation statements)

References 19 publications

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“…The last section of the present paper is dedicated to evaluate and validate the proposed method. Through four sets of tests, we will try to show the effectiveness and the discriminative power of the proposed method, compared with other well-known which are PANORAMA [11], LightField [9], Harmonics [16], and our previously proposed method multi-criteria with DEA [4].…”

Section: Resultsmentioning

confidence: 99%

“…Then, they use the spherical harmonics to discard orientation information. Our recent work has presented a new descriptor for 3D content-based shape retrieval [4]. The proposed approach was based on Data Envelopment Analysis (DEA) [17] to combine multiple geometric and topologic criteria of the 3D object for a global matching.…”

Section: View-based Approachesmentioning

confidence: 99%

“…This decomposition will reduce the complexity and highlight the topology of the 3D model. Then we calculate for each object, the different descriptors associated with each segment using our descriptor cited before [4]. In the online step, we propose a new strategy for computing the similarity between the segments of the request object and objects in the database.…”

Section: Fig 2 An Example Of Segmentation Obtainedmentioning

confidence: 99%

“…In this article, we propose to use a similarity search technique that compares the similarity between portions of 3D models rather than a global comparison. Using our approach, each 3D model is divided into its meaningful parts using significant segmentation methods presented recently in the literature, after segmenting the 3D objects we calculate for each part its descriptor by the method based on multi-criteria using DEA [4]. Two models are considered as similar if their segments are similar.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for partial shape matching and similarity based on data envelopment analysis

et al. 2019

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Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching.

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Section: Resultsmentioning

confidence: 99%

Section: View-based Approachesmentioning

confidence: 99%

Section: Fig 2 An Example Of Segmentation Obtainedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel approach for partial shape matching and similarity based on data envelopment analysis

et al. 2019

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“…This approach furnishes satisfying results, however, the shape index is not defined for flat faces, and it needs a pretreatment step for meshes that are not orientable or not topologically correct. Bouksim et al [11] presented a compact descriptor by using the Data envelopment analysis method to combine three criteria (dihedral angle, shape index, and the Shape Diameter Function). The principal inconvenient of this method is that it takes relatively a long time to generate the descriptors, since it uses three features, and combines them using DEA.…”

Section: Geometry Based Descriptorsmentioning

confidence: 99%

A Novel Partial 3D Object Retrieval Method Using Adaptive Slices Clustering

Taybi¹,

Bouksim²,

Alaoui³

et al. 2019

IJIES

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The similarity measuring between 3D objects can be reduced to a distance between their descriptors. Therefore, the principal challenge is the mapping of objects into reduced and compact representations referred to as descriptors. Generally speaking, descriptors based on global features cannot be effective to describe incomplete 3D objects. This paper presents a novel partial 3D object retrieval approach called adaptive slices clustering (ASC). To ensure that similar objects will be decomposed similarly, normalization is applied to the objects as a preprocessing step. Then, we characterize each object by a set of 2D slices corresponding to its three principal axes, transforming the shape-matching problem between objects into similarity measuring between their slices. Next, we choose among the extracted 2D slices the most representative to reduce searching time without diminishing the performance of our approach. The results show that our approach provides better performance than the descriptors that characterize the whole object.

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New 3D Objects Retrieval Approach using Multi Agent Systems and Artificial Neural Network

Sirbal¹,

Bouksim²,

Arhid³

et al. 2018

ijacsa

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Content-based 3D object retrieval is a substantial research area that has drawn a significant number of scientists in last couple of decades. Due to the rapid advancement of technology, 3D models are more and more accessible yet it is hard to find, the models we are searching for. This created the need for efficient and robust retrieval methods, allowing the extraction of relevant matches from the human perspective. Hence, in this paper we are proposing a new framework for 3D object retrieval that starts with a pre-treatment consisting of an Artificial Neural Network (ANN) algorithm with Histogram of features, allowing us to extract a representative value for each category of the database. These values are used for the Multi Agents System (MAS). In this phase, we are classifying these categories according to their relevance to the request object. This sets a distinguishing weight for each object of the database allowing us to extract the right matches. Experiments have proven the stringent of this approach. Keywords-3D object retrieval; 3D image processing; distributed artificial intelligence; multi-agent systems; artificial neural network (ANN)I.

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New Approach for 3D Mesh Retrieval Using Data Envelopment Analysis

Cited by 7 publications

References 19 publications

A novel approach for partial shape matching and similarity based on data envelopment analysis

A novel approach for partial shape matching and similarity based on data envelopment analysis

A Novel Partial 3D Object Retrieval Method Using Adaptive Slices Clustering

New 3D Objects Retrieval Approach using Multi Agent Systems and Artificial Neural Network

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