Split Semantic Detection in Sandplay Images

TL;DR

This paper introduces an automatic detection model for identifying 'split' semantics in sandplay images, aiming to replace manual analysis with a more efficient, visual-based approach that captures clients' psychological states.

Contribution

The work presents a novel distribution map generation method and feature extraction algorithm tailored for semantic detection in sandplay images, along with a newly built labeled dataset.

Findings

The proposed model effectively detects split semantics in sandplay images.

Experimental results show high accuracy and robustness of the method.

The dataset supports future research in psychoanalytic image analysis.

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.