Identification of Relevant Images from Ancient Paintings using Statistical Distribution

Objectives: This article highlights a novel concept of identifying the ancient images along with the artists by proposing a model based on a Generalized Bivariate Laplacian Mixture Model (GBLMM) approach for this purpose. Methods/Statistical Analysis: Conservative Chinese paintings emulate the exquisiteness of Chinese sculpture. Most of these descriptions are digitized and are available in the internet. One of challenging task is to retrieve the images of significance more efficiently and proficiently. This article highlights a pioneering idea to spot these ancient images along with the artist by proposing a model based on a Generalized Bivariate Laplacian Mixture Model (GBLMM) approach. Findings: The methodology is subjected to the application of ancient Chinese paintings. The results derived are evaluated against quality metrics such as image fidelity, peak signal noise ratio, structured coefficients, average difference and mean squared error. The proposed model accomplished over 97% of classification accuracy over a dataset containing 3750 traditional Chinese paintings. Application/Improvements: In order to ascertain the quality of segmentation, metrics like Image Fidelity (IF), Mean Squared Error (MSE) and Peak Signal Noise Ratio (PSNR) are considered. This model can be very much useful for the archeologists to identify the ancient paintings.