Journal of Multimedia Processing and Technologies Nguyen Minh Tuan 17 4 2026 https://doi.org/10.6025/jmpt/2026/17/4/208-226 https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_2.pdf The automation of interior design analysis encompassing style classification, composition optimization, and art placement relies heavily on machine learning (ML) models. However, the field currently lacks domainspecific, interpretable benchmarks that connect low level visual metrics with high level design semantics. While Deep Neural Networks (DNNs) achieve high accuracy in general image classification, their “black box” nature obscures which specific visual elements (e.g., color harmony, spatial topology, texture) actually define a design style. This paper introduces a comparative machine learning benchmark to evaluate the sufficiency of engineered visual features for reliable interior design style classification. Utilizing the Interior Spatial Sensor Dataset (4,141 records), we engineer a multidimensional feature vector encompassing chromatic distributions, textural metrics (GLCM), and geometric edge density. We evaluate a diverse suite of traditional ML and shallow neural network classifiers (Logistic Regression, Random Forest, SVM, Gradient Boosting, and MLP) using a multi-dimensional assessment protocol that includes accuracy, macro-F1, OvR ROC-AUC, and SHAP based interpretability. By establishing this rigorous evaluation framework, this study provides a blueprint for transitioning from opaque, computationally heavy deep learning architectures to transparent, lightweight models suitable for edge computing in smart home design applications. Ultimately, this benchmark addresses the scarcity of usable scientific ML benchmarks in spatial aesthetics, ensuring that automated design tools remain aligned with human perceptual and functional constraints.