Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
This is an incremental improvement for image retrieval systems.
The paper tackled texture analysis for Content Based Image Retrieval by introducing a new trace feature from Gray Level Co-occurrence Matrices and extending GLCM to n-dimensional images, showing that trace features outperform Haralick features in CBIR.
Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.