scholarly journals Computer Graphic and Photographic Image Classification using Local Image Descriptors

2017 ◽  
Vol 67 (6) ◽  
pp. 654 ◽  
Author(s):  
Gajanan K Birajdar ◽  
Vijay H Mankar
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Graphic ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Graphic Images

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

Optimized Feature Subset Selection and Relevance Feedback for Image Retrieval Based on Multiresolution Enhanced Orthogonal Polynomials Model

2015 ◽  
Vol 6 (2) ◽  
pp. 25-40
Author(s):  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Relevance Feedback ◽  
Nearest Neighbor ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Multi Objective Genetic Algorithm

In this paper, a simple image retrieval method incorporating relevance feedback based on the multiresolution enhanced orthogonal polynomials model is proposed. In the proposed method, the low level image features such as texture, shape and color are extracted from the reordered orthogonal polynomials model coefficients and linearly combined to form a multifeature set. Then the dimensionality of the multifeature set is reduced by utilizing multi objective Genetic Algorithm (GA) and multiclass binary Support Vector Machine (SVM). The obtained optimized multifeature set is used for image retrieval. In order to improve the retrieval accuracy and to bridge the semantic gap, a correlation based k-Nearest Neighbor (k-NN) method for relevance feedback is also proposed. In this method, an appropriate relevance score is computed for each image in the database based on relevant and non relevant set chosen by the user with correlation based k-NN method. The experiments are carried out with Corel and Caltech database images and the retrieval rates are computed. The proposed method with correlation based k-NN for relevance feedback gives an average retrieval rate of 94.67%.

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