Shape-based image retrieval of Chinese paper-cutting using RBFNN with invariant moment

Zheng, Timothy Tian-Ming; Ng, Wing W. Y.; Huang, Xusheng; Yang, Shi-Ting; Chan, Patrick P. K.; Lai, Weiwei; Yeung, Daniel

doi:10.1109/icmlc.2010.5580582

Cited by 2 publications

(1 citation statement)

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“…The generalized multi class color image classification has been addressed by some researchers [16], [17] but their results are not very good. The features used by image classification are varied and can be divided into color based features [9], [16], [17], shape based features [18]- [20], region based features [21], color moments [16], texture based features [22], [23], statistical features [24], DCT and DWT coefficients [13], [25]. There is no definite agreement on the selection of features for best results.…”

Section: Previous Work and Problem Identificationmentioning

confidence: 99%

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

Husain¹

2015

IJIEE

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The exponential growth in image data over the internet has resulted in a growing need for searching images according to our requirements. Content based image retrieval systems extract similar images from databases or the internet for facilitation of their users. A number of different feature sets and classifiers have been used by researchers for content based image retrieval. The goal of this research is to evaluate some common features sets used for classification of images and identify the best features depending upon the user requirement. Some commonly used features have been studies and a set of six feature sets have been selected for evaluation by the Back-Propagation Neural Network (BPNN). The results have been evaluated on the basis of precision and recall and it can be concluded that for natural images none of the feature sets perform well universally on all classes and the selection of optimal feature set depends on the type/class of images.

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Section: Previous Work and Problem Identificationmentioning

confidence: 99%

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

Husain¹

2015

IJIEE

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Evaluating the Efficiency of Paper-Cutting Cultural Symbol's Attributes Using ANP Approach

Chai¹,

Bao²

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

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Shape-based image retrieval of Chinese paper-cutting using RBFNN with invariant moment

Cited by 2 publications

References 13 publications

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

Evaluating the Efficiency of Paper-Cutting Cultural Symbol's Attributes Using ANP Approach

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