Research on Thangka Image Retrieval Algorithm Based on Mean Hashing

Research of Online Illustration Communication Platform

“…The core of the search process is how to obtain the fingerprint of the image. Among them, perceptual hash algorithm [1,2], average hash algorithm [3,4] or difference hash algorithm [5] and various improved algorithms [6][7][8] can be used to extract file features and generate fingerprints that can be used for comparison, and then compare and retrieve media resources such as pictures and videos. The average hash algorithm is a hash algorithm based on low-frequency signals, which is fast and easy to implement, and is also the algorithm used in this paper.…”

Thangka Image Segmentation Method Based on Enhanced Receptive Field

“…In the literature [19], a headdress segmentation algorithm based on circular region localization for portrait-like thangka images is proposed, which uses a circle detection algorithm to locate the approximate location of the headlight based on the characteristics of the circular headlight region and then uses the spatial distribution of headlight color and the exterior contour features of the headdress to achieve the primary dzong headdress segmentation, which cannot be detected for thangka images without a circular headlight, impure headlight color, and headdress with similar headlight color. The literature [20] proposed to use the maximum interclass variance method to obtain the threshold value to segment the image, and then the overall features are inscribed according to the Euler number of the headdress region and the color distribution inside the perspective contour; the literature [21] proposed the Mean-Shift-based tangka segmentation algorithm according to the problems such as the high computational complexity of tangka images and the difficulty of large-scale image segmentation, which combines the quadratic watershed The algorithm combines the quadratic watershed algorithm with the clustering theory of the improved weight matrix algorithm, which effectively reduces the computational complexity of the traditional clustering method; literature [22] proposes a headdress detection algorithm based on saliency mapping of thangka figures, which uses the attention model to calculate the saliency value of each pixel, and then detects the segmented headdress by finding the backlight region of the thangka; The segmentation of thangka images with high complexity using traditional image segmentation methods is not satisfactory; therefore, feature extraction of thangka images using deep learning methods and pixel classification using classifiers can result in highly accurate image segmentation. Literature [23] uses a line drawing enhancement module and a halving region generation network to segment thangka images.…”