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Interpretable Image Classification Using Attribute-Based KNN with Handcrafted Visual and Spatial Features
Muhammad Ismail, Zulfiqar Ali
Pages - 44 - 60 | Revised - 01-10-2025 | Published - 31-10-2025
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
Image Classification, Attribute-Based KNN, Handcrafted Features, Spatial Attributes, Interpretable Machine Learning.
ABSTRACT
Image classification remains a fundamental challenge in computer vision with applications in
retrieval, recognition, and scene understanding. This study introduces a transparent and
interpretable framework for image classification using the K-Nearest Neighbors (KNN) algorithm.
The approach leverages handcrafted visual features—color, pattern, shape, and texture—
together with spatial attributes derived from bounding box coordinates. These features are
encoded in a ternary scheme to represent presence, absence, or uncertainty, enabling consistent
similarity comparisons. The proposed model was systematically evaluated under varying kvalues,
multiple distance metrics (Euclidean, Cityblock, Cosine, and Correlation), and alternative
decision rules (Nearest, Consensus, Random). Experimental results demonstrate that the choice
of distance metric and neighborhood size significantly affects performance, with the Cityblock
metric and k = 1 yielding the highest accuracy. Importantly, the framework scales effectively to
larger datasets while maintaining strong interpretability, offering a balanced alternative to opaque
deep learning models. These findings highlight the potential of attribute-based KNN as a
lightweight, human-understandable solution for image classification in both research and
resource-constrained practical applications.
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Mr. Muhammad Ismail
Department of Computer Science, NFC Institute of Engineering and Fertilizer Research (NFC-IEFR), Faisalabad, 38000 - Pakistan
muhammad.ismail@iefr.edu.pk
Mr. Zulfiqar Ali
Department of Computer Science, NFC Institute of Engineering and Fertilizer Research (NFC-IEFR), Faisalabad, 38000 - Pakistan
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