BAS: a perceptual shape descriptor based on the beam angle statistics

Arıca, Nafiz; Vural, Fatoş T. Yarman

doi:10.1016/s0167-8655(03)00002-3

Cited by 166 publications

(136 citation statements)

References 13 publications

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“…But if the results of the curvature are acceptable, with descriptors of higher dimensionality we can be more confident that AESA will have a good behaviour. We want to remark as well that our proposal significantly speeds up the classification and retrieval of these shape descriptors [2][3][4][5][6][7][8] and that our heuristic is the only alternative to an exhaustive search for them. Of course, this proposal can be applied to other contexts based on DTW, not just the one of shape recognition and obviously it is possible to use other indexing methods based on metric spaces.…”

Section: Discussionmentioning

confidence: 98%

“…The real world databases used were the MPEG-7 Core Experiment CE-Shape-1 (part B) [20] and the Silhouette database [21]. The shape descriptors were: curvature (as an example descriptor of one dimension for each point), BAS [2] (four dimensiones) and the shape contexts (SC) [5,6] (60 dimensions). The results achieved with these descriptors, and in particular the ones with the shape contexts, can be applied to other ones of similar characteristics from the bibliography [3,4,[6][7][8].…”

Section: Methodsmentioning

confidence: 99%

“…Among the methods to solve this problem the ones related to Dynamic Time Warping (DTW) [1] and descriptors of the contour with sequences of components of several dimensions have had a significant presence [2][3][4][5][6][7][8]. In general, these shape descriptors aim to have information from all of the contour with respect to each point, that is the reason for their large size (see Figure 1 for an example of the shape descriptor used in [6]).…”

Section: Introductionmentioning

confidence: 99%

“…heuristic methods to obtain this invariance, they are not suitable in most of the domains. Therefore, the literature accepts that to obtain a good starting point we must make the comparison between every possible starting point of the sequence [2,3,9,4]. Hence the necessity to use cyclic sequences and then CDTW (Cyclic DTW) arises.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, they do not use the algorithm of [10], but they make clusters of sequences based on their similarity, treating every possible starting point as a different sequence and using indexing methods with lower bounds of these clusters. This solution seems to be suitable just for shape descriptors with only one dimension (such as the curvature) and not for much more dimensions [2][3][4][5][6][7][8]. For instance, in [5], 60 dimensions are required for each point (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

Palazón-González

Marzal

2012

Lecture Notes in Computer Science

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Abstract. Cyclic Dynamic Time Warping (CDTW) is a good dissimilarity of shape descriptors of high dimensionality based on contours, but it is computationally expensive. For this reason, to perform recognition tasks, a method to reduce the number of comparisons and avoid an exhaustive search is convenient. The Approximate and Eliminate Search Algorithm (AESA) is a relevant indexing method because of its drastic reduction of comparisons, however, this algorithm requires a metric distance and that is not the case of CDTW. In this paper, we introduce a heuristic based on the intrinsic dimensionality that allows to use CDTW and AESA together in classification and retrieval tasks over these shape descriptors. Experimental results show that, for descriptors of high dimensionality, our proposal is optimal in practice and significantly outperforms an exhaustive search, which is the only alternative for them and CDTW in these tasks.

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Section: Discussionmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

Palazón-González

Marzal

2012

Lecture Notes in Computer Science

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Supervised pattern classification based on optimum‐path forest

Papa

Falcão

Suzuki

2009

Int J Imaging Syst Tech

366

254

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We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimumcost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multi-class, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types.

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Data clustering as an optimum‐path forest problem with applications in image analysis

Rocha

Cappabianco

Falcão

2009

Int J Imaging Syst Tech

125

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ABSTRACT:We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a connectivity function is maximized, such that each maximum of the pdf becomes root of an optimum-path tree (cluster), composed by samples ''more strongly connected'' to that maximum than to any other root. We discuss the advantages over other pdf-based approaches and present extensions to large datasets with results for interactive image segmentation and for fast, accurate, and automatic brain tissue classification in magnetic resonance (MR) images. We also include experimental comparisons with other clustering approaches.

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BAS: a perceptual shape descriptor based on the beam angle statistics

Cited by 166 publications

References 13 publications

A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

Supervised pattern classification based on optimum‐path forest

Data clustering as an optimum‐path forest problem with applications in image analysis

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