Category knowledge, skeleton-based shape matching and shape classification

Download

index.pdf

Date

2008

Author

Erdem, İbrahim Aykut

Metadata

Show full item record

Item Usage Stats

194
views

205
downloads

Skeletal shape representations, in spite of their structural instabilities, have proven themselves as effective representation schemes for recognition and classification of visual shapes. They capture part structure in a compact and natural way and provide insensitivity to visual transformations such as occlusion and articulation of parts. In this thesis, we explore the potential use of disconnected skeleton representation for shape recognition and shape classification. Specifically, we first investigate the importance of contextual information in recognition where we extend the previously proposed disconnected skeleton based shape matching methods in different ways by incorporating category knowledge into matching process. Unlike the view in syntactic matching of shapes, our interpretation differentiates the semantic roles of the shapes in comparison in a way that a query shape is being matched with a database shape whose category is known a priori. The presence of context, i.e. the knowledge about the category of the database shape, influences the similarity computations, and helps us to obtain better matching performance. Next, we build upon our category-influenced matching framework in which both shapes and shape categories are represented with depth-1 skeletal trees, and develop a similarity-based shape classification method where the category trees formed for each shape category provide a reference set for learning the relationships between categories. As our classification method takes into account both within-category and between-category information, we attain high classification performance. Moreover, using the suggested classification scheme in a retrieval task improves both the efficiency and accuracy of matching by eliminating unrelated comparisons.

Subject Keywords

Electronic computers.

URI

http://etd.lib.metu.edu.tr/upload/3/12610118/index.pdf
https://hdl.handle.net/11511/18106

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Recursive shortest spaning tree algorithms for image segmentation Bayramoğlu, Neslihan Yalçın; Bazlamaçcı, Cüneyt Fehmi; Department of Electrical and Electronics Engineering (2005) Image segmentation has an important role in image processing because it is a tool to obtain higher level object descriptions for further processing. In some applications such as large image databases or video image sequence segmentations, the speed of the segmentation algorithm may become a drawback of the application. This thesis work is a study to improve the run-time performance of a well-known segmentation algorithm, namely the Recursive Shortest Spanning Tree (RSST). Both the original and the fast RSST...
Part embedding for shape grammars Yalım Keleş, Hacer; Özkâr, Mine; Department of Computer Engineering (2010) Computational modeling of part relations of shapes is a challenging problem that has been addressed by many researchers since sixties. The most important source of the difficulty is the continuous nature of shapes, which makes the expression of shape very difficult in terms of discrete parts. When discrete parts are combined, they fuse and yield new parts, i.e. parts emerge. There is a number of methods that support emergent part detection. However all of these methods are based on strong assumptions in ter...
Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004) The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
Performance analysis of stacked generalization Özay, Mete; Yarman Vural, Fatoş Tunay; Department of Information Systems (2008) Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. This study consists of two major parts. In the first part, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers and the content of the training data. In the second part, based on the findings for a new class of algorithms, called Meta-Fuzzified Yield Value (...
Context-sensitive matching of two shapes Başeski, Emre; Tarı, Zehra Sibel; Department of Computer Engineering (2006) The similarity between two shapes is typically calculated by measuring how well the properties and the spatial organization of the primitives forming the shapes agree. But, when this calculations are done independent from the context, i.e. the whole set of shapes in the experiments, a priori significance to the primitives is assigned, which may cause problematic similarity measures. A possible way of using context information in similarity measure between shape A and shape B is using the category informatio...

Citation Formats

İ. A. Erdem, “Category knowledge, skeleton-based shape matching and shape classification,” Ph.D. - Doctoral Program, Middle East Technical University, 2008.