@article{4843,
  author = {Nguyen Minh Tuan},
  title = {Evaluating Visual Feature Sufficiency for Interior Design Style Classification: A Comparative Machine Learning Benchmark},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jmpt/2026/17/4/208-226},
  url = {https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_2.pdf},
  abstract = {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.},
}