A Split Semantic Detection Algorithm for Psychological Sandplay Image

Psychological sandplay, as an important psychological analysis tool, is a visual scene constructed by the tester selecting and placing sand objects (e.g., sand, river, human figures, animals, vegetation, buildings, etc.). As the projection of the tester's inner world, it contains high-level semantic information reflecting the tester's thoughts and feelings. Most of the existing computer vision technologies focus on the objective basic semantics (e.g., object's name, attribute, boundingbox, etc.) in the natural image, while few related works pay attention to the subjective psychological semantics (e.g., emotion, thoughts, feelings, etc.) in the artificial image. We take the latter semantics as the research object, take "split" (a common psychological semantics reflecting the inner integration of testers) as the research goal, and use the method of machine learning to realize the automatic detection of split semantics, so as to explore the application of machine learning in the detection of subjective psychological semantics of sandplay images. To this end, we present a feature dimensionality reduction and extraction algorithm to obtain a one-dimensional vector representing the split feature, and build the split semantic detector based on Multilayer Perceptron network to get the detection results. Experimental results on the real sandplay datasets show the effectiveness of our proposed algorithm.