A skin detection approach based on the Dempster–Shafer theory of evidence

Shoyaib, Mohammad; Abdullah-Al-Wadud, M.; Chae, Oksam

doi:10.1016/j.ijar.2012.01.003

Cited by 43 publications

(23 citation statements)

References 61 publications

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“…Earlier, DS theory based combination has been applied on different fields like handwritten digit recognition [41], skin detection [42], 3D palm print recognition [43] among other pattern recognition domains.…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

et al. 2018

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Script identification is an essential step in document image processing especially when the environment is multi-script/multilingual. Till date researchers have developed several methods for the said problem. For this kind of complex pattern recognition problem, it is always difficult to decide which classifier would be the best choice. Moreover, it is also true that different classifiers offer complementary information about the patterns to be classified. Therefore, combining classifiers, in an intelligent way, can be beneficial compared to using any single classifier. Keeping these facts in mind, in this paper, information provided by one shape based and two texture based features are combined using classifier combination techniques for script recognition (word-level) purpose from the handwritten document images. CMATERdb8.4.1 contains 7200 handwritten word samples belonging to 12 Indic scripts (600 per script) and the database is made freely available at https://code.google.com/p/cmaterdb/. The word samples from the mentioned database are classified based on the confidence scores provided by Multi-Layer Perceptron (MLP) classifier. Major classifier combination techniques including majority voting, Borda count, sum rule, product rule, max rule, Dempster-Shafer (DS) rule of combination and secondary classifiers are evaluated for this pattern recognition problem. Maximum accuracy of 98.45% is achieved with an improvement of 7% over the best performing individual classifier being reported on the validation set.

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Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

et al. 2018

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“…Unlike Artificial Neural Networks, it uses many levels to represent highly nonlinear and highly varying functions. Usually, Deep Learning requires a large amount of training data to build the model from scratch and a number of training iterations for better performance [51][52][53]. …”

Section: Texture Based Classificationmentioning

confidence: 99%

An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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“…RGB color space was adopted due to its simplicity, effectiveness, speed and retaining of all color information [53]. In addition, RGB is one of the two primary color spaces used to represent digital component video.…”

Section: Skin Color Modelmentioning

confidence: 99%

Face detection using combination of skin color pixel detection and Viola-Jones face detector

Luh

2014

2014 International Conference on Machine Learning and Cybernetics

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This paper presents a human face detection scheme by combining a novel hybrid color models and Viola-Jones face detector. A hybrid skin color model RGB-CbCrCg was proposed for classifying skin and non-skin pixels. The extraction of skin region is carried out using a set of bounding rules optimized employing multi-objective differential evolution method. Afterward the segmented face regions are identified using Viola-Jones algorithm built in MATLAB Computer Vision System Toolbox™. The performances of skin and face detection were evaluated using the ECU face and skin database. Experimental results show that the proposed scheme outperforms the other skin color space methods.

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A skin detection approach based on the Dempster–Shafer theory of evidence

Cited by 43 publications

References 61 publications

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

An Automated System for Garment Texture Design Class Identification

Face detection using combination of skin color pixel detection and Viola-Jones face detector

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