Classification of archaeological monuments for different art forms with an application to CBIR

Abstract

Until now, Content Based Image Retrieval (CBIR) techniques barely contributed to the archaeological domain. The use of these techniques can support archaeologists in their assessment and classification of archaeological finds. Museums and art galleries deal in inherently visual objects. The ability to identify objects sharing some aspect of visual similarity can be useful both to researchers trying to trace historical influences, and to art lovers looking for further examples of paintings or sculptures appealing to their taste. This paper illustrates the use of CBIR techniques for automatic classification of archaeological monuments using visual features shape and texture to study the art form and retrieve the similar images from reference collection. Shape based features are extracted using morphological operators and texture features are extracted using gray level co-occurrence matrix (GLCM). Robust feature set is built to retrieve the similar images. Experiments have been conducted on database consists of 500 images with 5 categories. Results of proposed method are compared with Canny and Sobel methods. Results demonstrate the efficiency of proposed method.