A hybrid feature fusion framework, termed DWT_AlexNet_DNN, is proposed, which combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet for texture image classification.
Abstract
Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture local texture characteristics but may have limited capability to represent complex visual patterns. In contrast, deep learning models automatically learn discriminative representations but may not fully exploit the multiscale spatial-frequency information inherent in texture images. This paper proposes a hybrid feature fusion framework, termed DWT_AlexNet_DNN, which combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet for texture image classification.
Image classification often requires representations that can capture both local texture details and higher-level visual information. In this paper, we propose a hybrid multi-level feature representation framework for better image classification by combining the handcrafted and deep features through a sequential process...
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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 representati...
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The DeepWave Feature Extractor (DW-FE) is proposed, a tri-branch framework integrating Haar wavelet decomposition for multi-resolution frequency features, pre-trained VGG16 for deep semantic representations, and a custom lightweight CNN for dataset-specific spatial patterns.
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This research proposes Adaptive Local Ternary Pattern (ALTP) that uses a dynamically calculated threshold to determine texture pattern for an image region that outperformed state-of-the-art texture feature descriptors.
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
