基于层次聚类的图文检索模型研究

A Hierarchical Clustering-Based Image-Text Retrieval Model

摘要:

图文检索在工业中的用途和作用是多方面的,可以帮助提高研发和生产效率,促进科技创新,提高产品的质量和竞争力；目前,图文检索模型的重点是提高检索的精度；随着技术和数据的快速发展,深度学习和大模型技术的不断应用,图文检索的速度问题逐渐凸显,为解决当前图文检索速度受限、计算量大的问题,提出了一种基于层次聚类的图文检索模型；该方法选择了检索效果明显的跨模态哈希方法,并运用深度聚类算法对待检索的数据进行分类,从而缩小检索范围,提高了检索速度；实验结果表明,基于层次聚类的图文检索模型在保持检索精度的同时,显著提高了检索速度,使得工程人员能够更快地获取到满意的检索结果。

Abstract:

The application and impact of image-text retrieval in industry are multifaceted, as it can help improve research and development efficiency, promote technological innovation, and enhance product quality and competitiveness. Currently, the emphasis of image-text retrieval models is on improving retrieval accuracy. With the rapid development of technology and data, the continuous application of deep learning and large-scale model techniques has gradually highlighted the issue of retrieval speed in image-text retrieval. To address the current limitations in retrieval speed and high computational requirements, a hierarchical clustering-based image-text retrieval model has been proposed. This method adopts a cross-modal hashing approach with evident retrieval effectiveness and applies deep clustering algorithms to classify the data to be retrieved, thereby reducing the retrieval scope and improving retrieval speed. Experimental results indicate that the hierarchical clustering-based image-text retrieval model significantly enhances retrieval speed while maintaining retrieval accuracy, enabling engineering personnel to obtain satisfactory retrieval results more quickly.