Recently, content-based image retrieval has been used for monitoring and tracking criminal behavior in images and videos. In addition, with the development of machine learning techniques, image searches using scene graphs have also been studied. The characteristic of scene graph data is that there are very many dimensions of the data. There is a problem that it is generally difficult to retrieve. In this paper, we propose a distributed in-memory-based high-dimensional indexing scheme. Wet can exploit the proposed scheme for similarity search using large high-dimensional vectors for content-based image retrieval and scene graphs. Furthermore, we propose a multi-level indexing scheme to utilize the master/slave model. In addition, we perform performance evaluations to demonstrate the superiority and validity of the proposed scheme.