Objective: This study proposes a hybrid framework for visual pattern recognition that combines computer vision-based feature extraction with supervised machine learning. The framework is designed to improve classification performance by integrating complementary image characteristics into a unified feature representation. Methods: The proposed framework comprises image preprocessing, visual feature extraction, feature integration, supervised model training and performance assessment. Input images undergo resizing, noise reduction and intensity normalization to minimize variations arising from image acquisition conditions. Four categories of visual information—edge structure, texture characteristics, colour distribution and key-point descriptors—are subsequently extracted to represent complementary spatial and appearance-related properties. The resulting descriptors are concatenated into a unified feature vector and supplied to a supervised classification model for pattern discrimination. The effectiveness of the integrated representation is assessed against individual feature-based configurations using classification performance across multiple visual categories and varying environmental conditions. Results: The experimental evaluation produced an average classification accuracy of 94.3%, with precision and recall remaining comparatively balanced across the evaluated categories. The integrated feature representation achieved higher classification performance than configurations based on individual feature types, indicating that complementary visual descriptors provide more informative representations for distinguishing visually similar patterns. The results also demonstrate greater consistency under variations in illumination, background conditions and image quality. Conclusion: The findings indicate that the integration of heterogeneous visual descriptors with supervised classification can provide a robust representation for visual pattern recognition. By combining structural, textural, chromatic and key-point information, the proposed framework addresses limitations associated with relying on a single type of image descriptor. The framework provides a systematic basis for developing recognition systems capable of maintaining reliable classification performance under diverse imaging conditions.

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