Algorithm for Extracting Cultural Elements in Free-hand Painting Creations Based on Local Contrast Analysis

This paper aims to clear up the shortcomings of earlier artwork in extracting cultural factors in free-hand brushwork, particularly in neighborhood contrast processing. We propose an algorithm for extracting cultural elements of free-hand painting based on neighborhood assessment. First, the element and texture of free-hand brushwork are more suitable by nearby histogram equalization, which lays a foundation for subsequent feature extraction. A convolutional neural network (CNN) is then used to robotically analyze and extract key functions from the enhanced photos, along with symbols, shades, texture patterns and identifiers of precise creative styles. These functions are used to efficiently classify free-hand brushwork. The test on massive-scale art database verifies the performance and accuracy of this algorithm inside the function extraction and category of free-hand brushwork. This study now not only presents a brand-new attitude for the evaluation of free-hand brushwork on the technical level, but additionally provides a sensible methodology for the automatic understanding and class of creative works.