Research on Block Image Texture Retrieval Method Based on Depth Hash

Jie Ding, G. Zhao, Jinyong Huang
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引用次数: 2

Abstract

In order to improve the retrieval ability of super-resolution multi-space block images, a texture retrieval method of block images based on depth hash is proposed. A texture feature analysis model of super-resolution multi-dimensional partitioned images is constructed, which combines texture spatial structure mapping method to realize depth information fusion of partitioned images, adopts edge feature detection and texture sparse feature clustering to realize texture hierarchical structure feature decomposition of super-resolution multi-dimensional partitioned images, and adopts deep image parameter analysis method to construct pixel structure recombination model of multi-dimensional partitioned images. Multi-dimensional texture parameter structure analysis and information clustering are realized for the collected partitioned images in multi-dimensional space. According to the information clustering results, the texture retrieval and extraction of partitioned images are realized by using the deep hash fusion algorithm, and the information detection and feature recognition capabilities of partitioned images in multi-dimensional space are improved. Simulation results show that this method has higher precision and better feature resolution in texture retrieval of partitioned images in multidimensional space, which improves the texture retrieval and recognition ability of partitioned images.
基于深度哈希的块图像纹理检索方法研究
为了提高超分辨率多空间块图像的检索能力,提出了一种基于深度哈希的块图像纹理检索方法。构建了超分辨率多维分割图像的纹理特征分析模型,结合纹理空间结构映射方法实现分割图像的深度信息融合,采用边缘特征检测和纹理稀疏特征聚类实现超分辨率多维分割图像的纹理分层结构特征分解;并采用深度图像参数分析方法构建多维分割图像的像素结构重组模型。对采集到的分割图像在多维空间中实现了多维纹理参数结构分析和信息聚类。根据信息聚类结果,利用深度哈希融合算法实现了分割图像的纹理检索和提取,提高了分割图像在多维空间中的信息检测和特征识别能力。仿真结果表明,该方法在多维空间分割图像的纹理检索中具有较高的精度和较好的特征分辨率,提高了分割图像的纹理检索和识别能力。
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