Application of Deep Belief Network under Similarity Constraints in Feature Extraction of Image Data

To address the poor accuracy and integrity of feature extraction caused by the failure of mining the prevalent structures of image data during the process of feature extraction of image data, this research proposes a method of image data feature extraction based on deep belief network (DBN) under similarity constraints. More specifically, the image data information is initially marked based on the introduced DBN. Subsequently, through mining the popular structure of image data employing the nearest-neighbor graph structure, matrix optimization technology is utilized to reduce the dimensionality of the mined image data, thereby obtaining the dimensionality reduction set of image data. Lastly, the feature extraction of image data is accomplished by leveraging the wavelet packet transform technology. The experimental results indicate that the proposed method significantly improves both the accuracy and integrity of feature extraction compared with the traditional feature extraction methods for image data, conclusively demonstrating the superior feature extraction performance of the proposed method as well as its feasibility for widespread application across diverse data processing scenarios.