Cognitive linguistics has provided compelling evidence that semantic structure in natural language reflects conceptual structure that arises from our embodied experience in the world. To capture this conceptual structure, a set of spatio-temporal cognitive building blocks called image schemas was introduced by Lakoff and Johnson. Detecting image schemas in natural language can provide further insights into how embodied experiences are encoded in natural language and potentially contribute to research on conceptual understanding and symbol grounding in cognitive systems. Methods for (semi-)automatically extracting image schemas from natural language are an open challenge. We propose a spectral clustering approach paired with semantic role labeling to semi-automatically extract image schemas from multilingual text, obtaining a precision of more than 80% on three languages.