Optimal partial shape similarity

Latecki, Longin Jan; Lakaemper, Rolf; Wolter, Diedrich

doi:10.1016/j.imavis.2004.06.015

Cited by 51 publications

(30 citation statements)

References 22 publications

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“…[5][6][7][8]33], in that they cannot be easily extended to handle similarity of contour parts. Only a small number of approaches address the issue of partial shape similarity [9][10][11]30,31]. The existing partial shape similarity measures (e.g., Ref.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Detection and recognition of contour parts based on shape similarity

Bai

Yang

Latecki

2008

Pattern Recognition

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Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation. ᭧ 2008 Elsevier Ltd. All rights reserved.Keywords: Shape similarity; Parts of visual form; Detection of contour parts System overviewWe begin with an overview of the system for contour-based object recognition. Based on psychophysical evidence [1], we can derive the following stages of contour-based object recognition (see Fig. 1):We do not discuss edge detection here, since it is an obvious step in image analysis that is also known to be performed by the human visual system. We view contour grouping as grouping of edge pixels to contour parts. It is a local process based on rule of good continuation (see Ref. comparing (b) and (c) in Fig. 1, contour grouping also means contour simplification and removal of small irrelevant features in our approach. We describe it in Section 5.In the proposed system contour grouping is followed by contour detection, described in Section 6. For each contour part produced by contour grouping [32], the most similar contour parts in a database of known contour segments are retrieved using shape similarity. Therefore, contour detection also yields preliminary recognition results of contour parts, which can be viewed as recognition hypothesis. This fact is illustrated in Fig. 2. The second column shows most significant contour parts detected in images shown in first column. The contour part significance ranking is based on shape similarity to know contour parts, which are shown in columns 3-7. If we use first nearest neighbor classifier (1NN), then column 3, which shows the most similar database contour segments, illustrates the recognition results. Thus, contour part detection and recognition are based on shape similarity to know contour parts. Observe that the objects in all three query images are correctly recognized based

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Section: Motivation and Backgroundmentioning

confidence: 99%

Detection and recognition of contour parts based on shape similarity

Bai

Yang

Latecki

2008

Pattern Recognition

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“…The inner distance is defined as the length of the shortest path within the shape boundary to classify shape images. The approach presented in [22] is aimed at handling partially visible shapes. Some methods make use of curve alignment techniques [31].…”

Section: Previous Workmentioning

confidence: 99%

Tree-based binary image dissimilarity measure with meta-heuristic optimization

Zieliński

Iwanowski

2015

Pattern Anal Applic

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In this paper, we present a method of evaluating binary image dissimilarity based on tree representation and heuristic optimization. Starting from the image, a graph structure of a binary tree is constructed that splits the set of image foreground pixels into consecutive subsets attached to tree nodes. Next, instead of comparing two images themselves, one compares the trees and expresses image dissimilarity as tree dissimilarity, which can be characterized by a nonlinear function. The goal is to find its minimum, as it corresponds with the best match of compared trees. Searching for the minimum would be ineffective with analytical optimization methods. Hence, we have approached the issue with three meta-heuristic algorithms, namely genetic algorithm, particle swarm optimization (PSO) and simulated annealing. The presented results show that PSO achieved the best results. The proposed method is compared with other binary image comparison approaches. The performed tests that are described in the paper show that it outperforms its competitors and can be successfully applied to compare binary images.

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“…From this point of view, our approach is close in its spirit to the method of Latecki et al (2005), Bronstein et al (2008b), Bronstein and Bronstein (2008) for partial shape matching, using a tradeoff between the size of the parts cropped out of the shapes and the similarity between them. In our case, the set of all Pareto optimal solutions can also be represented as a set-valued similarity criterion, which contains much richer information than each of the intrinsic and extrinsic criteria separately.…”

Section: Fig 2 (Color Online)mentioning

confidence: 99%

Topology-Invariant Similarity of Nonrigid Shapes

Bronstein

Kimmel

2008

Int J Comput Vis

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This paper explores the problem of similarity criteria between nonrigid shapes. Broadly speaking, such criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the object and the latter to how it is laid out in the Euclidean space. Both criteria have their advantages and disadvantages: extrinsic similarity is sensitive to nonrigid deformations, while intrinsic similarity is sensitive to topological noise. In this paper, we approach the problem from the perspective of metric geometry. We show that by unifying the extrinsic and intrinsic similarity criteria, it is possible to obtain a stronger topology-invariant similarity, suitable for comparing deformed shapes with different topology. We construct this new joint criterion as a tradeoff between the extrinsic and intrinsic similarity and use it as a set-valued distance. Numerical results demonstrate the efficiency of our approach in cases where using either extrinsic or intrinsic criteria alone would fail.

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Optimal partial shape similarity

Cited by 51 publications

References 22 publications

Detection and recognition of contour parts based on shape similarity

Detection and recognition of contour parts based on shape similarity

Tree-based binary image dissimilarity measure with meta-heuristic optimization

Topology-Invariant Similarity of Nonrigid Shapes

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