Split Semantic Detection in Sandplay Images

Abstract: Sandplay image, as an important psychoanalysis carrier, is a visual scene constructed by the client selecting and placing sand objects (e.g., sand, river, human figures, animals, vegetation, buildings, etc.). As the projection of the client's inner world, it contains high-level semantic information reflecting the client's subjective psychological states, which is different from the common natural image scene that only contains the objective basic semantics (e.g., object's name, attribute, bounding box, etc.). In this work, we take "split" which is a typical psychological semantics related to many emotional and personality problems as the research goal, and we propose an automatic detection model, which can replace the time-consuming and expensive manual analysis process. To achieve that, we design a distribution map generation method projecting the semantic judgment problem into a visual problem, and a feature dimensionality reduction and extraction algorithm which can provide a good representation of split semantics. Besides, we built a sandplay datasets by collecting one sample from each client and inviting 5 therapists to label each sample, which has a large data cost. Experimental results demonstrated the effectiveness of our proposed method.