A study on a content-based image retrieval technique for Chinese paintings

Abstract

Purpose The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search images similar to the query image based on colour histogram and texture features in the database. Thus, retrieving images by this image technique is expected to make the retrieval of Chinese painting images more precise and convenient for users. Design/methodology/approach In this study, a technique is proposed that considers spatial information of colours in addition to texture feature in image retrieval. This technique can be applied to retrieval of Chinese painting images. A database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. The authors develop an image-retrieval technique that considers colour distribution, spatial information of colours and texture. Findings In this study, a database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. An image-retrieval technique was developed that considers colour distribution, spatial information of colours and texture. Through adjustment of feature values, this technique is able to process both landscape and portrait images. This technique also addresses liubai (i.e. blank) and text problems in the images. The experimental results confirm high precision rate of the proposed retrieval technique. Originality/value In this paper, a novel Chinese painting image-retrieval technique is proposed. Existing image-retrieval techniques and the features of Chinese painting are used to retrieve Chinese painting images. The proposed technique can exclude less important image information in Chinese painting images for instance liubai and calligraphy while calculating the feature values in them. The experimental results confirm that the proposed technique delivers a retrieval precision rate as high as 92 per cent and does not require a considerable computing power for feature extraction. This technique can be applied to Web page image retrieval or to other mobile applications.

Application of Digital Painting Technology in the Creation of an Oil Painting Containing Graphene

“…Hung established a digital Chinese traditional painting image database, used the proposed technology to extract image and texture information, and searched for images similar to the query image based on the color histogram and texture features in the database. Hung expects that using this image technology to retrieve images that will make the retrieval of Chinese painting images more accurate and user-friendly, but the database lacks some humanized design [4]. Based on the experience of the Church in Cappadocia, Turkey, Higuchi et al proposed a new method to record the conditions of preservation and restoration of on-site frescoes.…”

The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background

“…In the 21st century, under the background of new media, the creators of traditional Chinese landscape paintings have inherited the past, learned the essence of ancient paintings, and injected the elements of the new era of paintings on the basis of the concept of "Western and Chinese" paintings, and then created a distinctive pictorial language and style of paintings, and at the same time, embodied the ethnicity, epochal and inheritance of the Chinese landscape paintings [11][12]. Analyzing the use of visual psychology in traditional Chinese landscape painting from the perspective of visual perception and sorting out the mechanism of visual psychology of contemporary Chinese landscape painting creators should provide important references for the creation and theoretical research of contemporary Chinese landscape painting [13][14].…”

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

“…Within the analysis of static images, art has been a field for experimentation and analysis regarding emotions. Thus, some studies have used abstract art (Chiarella et al, 2022; Sartori et al, 2015; Yanulevskaya et al, 2012; Zhang et al, 2011; Zhao et al, 2014), art styles including Oriental art (Hung, 2018), cubist art (Ginosar, Haas, Brown, & Malik, 2014), figurative art (Hagtvedt, Patrick, & Hagtvedt, 2008; Huang, Huang, & Kuo, 2010), or artworks from various cultures (Stamatopoulou & Cupchik, 2017), among others. Similarly, in this field, experiments have been done combining different resources apart from image analysis, such as using the title of the work or the author (Chamberlain, Mullin, Scheerlinck, & Wagemans, 2018; Chiarella et al, 2022; Huang, Bridge, Kemp, & Parker, 2011; Sartori et al, 2015; Tashu, Hajiyeva, & Horvath, 2021) to better define emotion.…”