Image Feature Fusion Method Based on Edge Detection

Deep learning-based image processing algorithms have developed rapidly in the past decade and have shown significant improvements to extract image features when both sufficient computing power and big data are accessible. Thus, rapid advances in applications such as facial recognition and autonomous driving have been one of the implementation areas. On the other hand, edges as a low-level prevalence feature in images with independent semantics are practically adapted to attain better outcomes. However, neural network-based image feature extraction focusing on texture rather than shape leads to insufficient accuracy. To address this issue, an edge feature extraction method utilizing both conventional operators such as HDE and Sobel and a deep learning-based method is proposed to classify and retrieve images with better accuracy outcomes. By doing so, a large amount of data needed to conduct deep learning-based methods is decreased, the transferability of the model is achieved, classification and retrieval accuracies are enhanced, and the data is compressed. All these better results are attained with benchmark data sets. As a result, all these are achieved by proposing a novel method.