Re-Ranking Image Retrieval on Multi Texton Co-Occurrence Descriptor Using K-Nearest Neighbor

Abstract

Some features commonly used to conduct image retrieval are color, texture and edge. Multi Texton Co-Occurrence Descriptor (MTCD) is a method which uses all three features to perform image retrieval. This method has a high precision when doing retrieval on a patterned image such as Batik images. However, for images focusing on object detection like corel images, its precision decreases. This study proposes the use of KNN method to improve the precision of MTCD method by re-ranking the retrieval results from MTCD. The results show that the method is able to increase the precision by 0.8% for Batik images and 9% for corel images.

Classification of Pringgasela Typical Songket Using Multi Texton Co-occurrence Descriptor and K-Nearest Neighbor

“…Similar research has also been conducted that discusses the redesign of the image retrieval process using the MTCD and K-Nearest Neighbor (KNN) methods using batik and Corel data. The results of the study showed an increase in the precision of the classification results by 0.8% for batik data and 9% for Corel data [9]. This study shows that MTCD is very well used for processing color and texture extraction for fabric images.…”