Efficient segmentation for Region-based Image Retrieval using Edge Integrated Minimum Spanning Tree

Yang Liu, Lei Huang, Siqi Wang, Xianglong Liu, Bo Lang

Abstract: Region-based Image Retrieval (RBIR), which bases itself on image segmentation rather than global features or key-point-based local features, is a branch of Content-based Image Retrieval. This paper proposes a novel RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST). The difference between EI-MST and the traditional MST-based methods is that EI-MST generates MSTs over edge-maps rather than the original images, which achieved high retrieval performance cooperating with state-of-the-art matching strategies. In addition, by limiting the nodes in every MST with adaptive scale selection, EI-MST is efficient especially when processing high resolution images. The experiments on four popular public datasets proved that, EI-MST is capable of achieving higher retrieval accuracy over four widely used segmentation methods while only consuming moderate amount of time in both online and offline parts of RBIR systems.