Beyond English text: Multilingual and multimedia information retrieval.

Non

Some Issues in the Art Image Database Systems

In this paper we illustrate several aspects of art databases, such as: the spread of the multimedia art images; the main characteristics of art images; main art images search models; unique characteristics for art image retrieval; the importance of the sensory and semantic gaps. In addition, we present several interesting features of an art image database, such as: image indexing; feature extraction; analysis on various levels of precision; style classification. We stress color features and their base, painting analysis and painting styles. We study also which MPEG-7 descriptors are best for fine painting images retrieval. An experimental system is developed to see how these descriptors work on 900 art images from several remarkable art periods. On the base of our experiments some suggestions for improving the process of searching and analysis of fine art images are given

Monte Carlo convex hull model for classification of traditional Chinese paintings

While artists demonstrate their individual styles through paintings and drawings, how to describe such artistic styles well selected visual features towards computerized analysis of the arts remains to be a challenging research problem. In this paper, we propose an integrated feature-based artistic descriptor with Monte Carlo Convex Hull (MCCH) feature selection model and support vector machine (SVM) for characterizing the traditional Chinese paintings and validate its effectiveness via automated classification of Chinese paintings authored by well-known Chinese artists. The integrated artistic style descriptor essentially contains a number of visual features including a novel feature of painting composition and object feature, each of which describes one element of the artistic style. In order to ensure an integrated discriminating power and certain level of adaptability to the variety of artistic styles among different artists, we introduce a novel feature selection method to process the correlations and the synergy across all elements inside the integrated feature and hence complete the proposed style-based descriptor design. Experiments on classification of Chinese paintings via a parallel MCCH model illustrate that the proposed descriptor outperforms the existing representative technique in terms of precision and recall rates

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

Unveiling Patterns and Colors in Architectural Paintings: An Analysis by K-Means++ Clustering and Color Ratio Analysis

This study delves into the intricate world of patterns and colors found in architectural paintings within the illustrious Forbidden City. Through an in-depth analysis, we identified seven distinctive patterns, creating a pattern factor library that showcases five examples for each pattern category. To extract the color schemes of each architectural painting type, we employed the K-Means++ algorithm for secondary clustering. Utilizing both RGB and HSV color space models, we examined scatter diagrams and histograms for three specific architectural color paintings. The results revealed a balanced distribution of warm and cool colors across all three architectural painting types. The prevalent colors observed in the Forbidden City architectural paintings were red, yellow, cyan, and blue, exhibiting low levels of saturation and moderate to high levels of brightness, evoking a serene and luminous ambiance. Through color ratio analysis, we established traditional color names that corresponded to the extracted color values from each painting. Our findings suggest that the colors and patterns within the Forbidden City architectural paintings communicate a profound sense of tranquility and grandeur, aligning with the cultural and artistic values held during the Ming and Qing dynasties

MCCFNet: multi-channel color fusion network for cognitive classification of traditional Chinese paintings.

The computational modeling and analysis of traditional Chinese painting rely heavily on cognitive classification based on visual perception. This approach is crucial for understanding and identifying artworks created by different artists. However, the effective integration of visual perception into artificial intelligence (AI) models remains largely unexplored. Additionally, the classification research of Chinese painting faces certain challenges, such as insufficient investigation into the specific characteristics of painting images for author classification and recognition. To address these issues, we propose a novel framework called multi-channel color fusion network (MCCFNet), which aims to extract visual features from diverse color perspectives. By considering multiple color channels, MCCFNet enhances the ability of AI models to capture intricate details and nuances present in Chinese painting. To improve the performance of the DenseNet model, we introduce a regional weighted pooling (RWP) strategy specifically designed for the DenseNet169 architecture. This strategy enhances the extraction of highly discriminative features. In our experimental evaluation, we comprehensively compared the performance of our proposed MCCFNet model against six state-of-the-art models. The comparison was conducted on a dataset consisting of 2436 TCP samples, derived from the works of 10 renowned Chinese artists. The evaluation metrics employed for performance assessment were Top-1 Accuracy and the area under the curve (AUC). The experimental results have shown that our proposed MCCFNet model significantly outperform all other benchmarking methods with the highest classification accuracy of 98.68%. Meanwhile, the classification accuracy of any deep learning models on TCP can be much improved when adopting our proposed framework