Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symbolic patterns

Guru, D. S.; Kiranagi, Bapu B.

doi:10.1016/j.patcog.2003.06.006

Cited by 41 publications

(36 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conventional data analysis techniques may fail to capture such variations effectively. From the literature survey, we understand that the concept of symbolic data analysis has been well studied in the field of cluster analysis [7,8,9,10,11,12], shape analysis [13] and signature biometric applications [14]. Also suitability of symbolic data analysis approach for gait recognition is attempted recently in [15,16,17,18].…”

Section: Related Workmentioning

confidence: 99%

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Kumar¹,

Nagendraswamy²

2014

SIPIJ

View full text Add to dashboard Cite

Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking style. Gait can be recognized by studying the static and dynamic part variations of individual body contour during walk. In this paper, an interval value based representation and recognition of gait using local binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is split into four equal regions. LBP technique is applied to each region to extract features and the extracted features are well organized. The proposed representation technique is capable of capturing variations in gait due to change in cloth, carrying a bag and different instances of normal walking conditions more effectively. Experiments are conducted on the standard and considerably large database (CASIA database B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait recognition system. The proposed system being robust to handle variations has shown significant improvement in recognition rate.

show abstract

Section: Related Workmentioning

confidence: 99%

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Kumar¹,

Nagendraswamy²

2014

SIPIJ

View full text Add to dashboard Cite

show abstract

“…Symbolic data are more unified by means of relationships and they appear in the form of continuous ratio, discrete absolute interval, multi-valued and also multi-valued with weights [14]. The concept of symbolic data has been extensively studied in the area of cluster analysis [15,16,14,17,18,19,20,21] and it has been experimentally shown that the approaches based on symbolic data outperforms conventional data analysis approaches [22,15,16]. A symbolic approach to shape representation and recognition has been explored in [23] and the concept of symbolic data analysis has also been explored in online signature verification and recognition [24].…”

Section: Introductionmentioning

confidence: 99%

Change Energy Image for Gait Recognition: An Approach Based on Symbolic Representation

P¹,

Nagendraswamy²

2014

IJIGSP

View full text Add to dashboard Cite

-Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval -valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.

show abstract

“…However, in the literature many proximity measures have been proposed [ [7]. These proximity measures except for [5] [6], compute the proximity between symbolic objects in crisp symmetric form. However in [5] [6], the proximity is approximated in multivalued type which is not necessarily symmetric.…”

Section: Introductionmentioning

confidence: 99%

“…These proximity measures except for [5] [6], compute the proximity between symbolic objects in crisp symmetric form. However in [5] [6], the proximity is approximated in multivalued type which is not necessarily symmetric. In this paper the measure proposed in [6] is modified to suit the multivalued data type.…”

Section: Introductionmentioning

confidence: 99%

“…However in [5] [6], the proximity is approximated in multivalued type which is not necessarily symmetric. In this paper the measure proposed in [6] is modified to suit the multivalued data type. The proposed measure is used to compute the proximity of multivalued symbolic data types and the measure proposed in [6] for interval symbolic data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Symbolic Dissimilarity Measure for Multivalued Data Type and Novel Dissimilarity Approximation Techniques

Kiranagi¹,

Guru²

2010

IJCA

View full text Add to dashboard Cite

In this paper a new statistical measure for estimating the degree of dissimilarity between two symbolic objects whose features are multivalued type is proposed. In addition two new simple representation techniques viz., interval type and magnitude type for the computed dissimilarity between the symbolic objects are introduced. The dissimilarity matrices obtained are not necessarily symmetric. Hence, clustering algorithms to work on such unconventional approximated matrices, by introducing the concept of mutual average dissimilarity value and magnitude average dissimilarity respectively for interval type and magnitude type approximation representations are also proposed.

show abstract

Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symbolic patterns

Cited by 41 publications

References 5 publications

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Change Energy Image for Gait Recognition: An Approach Based on Symbolic Representation

A New Symbolic Dissimilarity Measure for Multivalued Data Type and Novel Dissimilarity Approximation Techniques

Contact Info

Product

Resources

About