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

Yufis Azhar, Agus Eko Minarno, Yuda Munarko

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.

Keywords


Batik; Image Retrieval; Re-ranking; Multi Texton Co-Occurrence Descriptor; GLCM; K-Nearest Neighbor

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