Classification of Iranian Paintings Using Texture Analysis

in recent years, digital painting collections are available to the public and this is growing in museums digital galleries. With the availability of large collections of digital, it is essential to develop multimedia systems for archiving and retrieving them. Recognition the style of each artist is one of the key issues, however, most artists do not identify their styles. Traditionally, people empirically recognize an artist's style through following the artist's paintings and investigating to the paintings' details. This paper is proposed one different approach in order to identifY Iranian painters' style by image processing techniques for the first time. We use texture analysis for doing classification. The extracted features are the local binary pattern (LBP), Local phase quantization (LPQ) and local configuration pattern (LCP). To assess the proposed method, one database of paintings that contains five famous Iranian painters namely Hossein Behzad, KamalolMolk, Morteza Katouzian, Sohrab Sepehri and Mahmoud Farshchian is utilized. The experimental results verify reasonably good performance.