基于“点到面”策略的图像检索

“Point-to-Flat”Strategy-Based Image Retrieval

图像检索是计算机视觉领域的一个重要分支。其主要目的是从图像数据库中找出与查询图像相似的语义图像。传统的图像检索方法是在查询图像和数据库图像之间进行“点到点”检索。但是,单个查询图像包含的类别提示较少,即类别信息较弱,使得检索结果并不理想。为了解决这个问题,本文提出了一种基于“点到面”的类别检索策略来扩展一个图像(点)到一个图像类别(面),这意味着从单个查询图像到整个图像类别的语义扩展。该方法挖掘了查询图像的类别信息。在两个常用的数据集上对所提出方法的性能进行了评估。实验表明,该方法可以显著提高图像检索的性能。

Image retrieval is an important branch of computer vision.Its main purpose is to find the similar semantic images to the query image from the image database.The traditional image retrieval method is a“point-to-point”retrieval between the query image and the database.However,a single query image contains fewer category hints,that is,the category information is weaker,and the retrieval results are not satisfactory.In this paper,the“point-to-flat”category-based retrieval strategy is proposed to extend an image(point)to an image category(flat),which means the semantic extension from the individual query image to the whole image category.The proposed method mines the category information of the query image.The performance of the proposed method is evaluated on two commonly used databases.The experimental results demonstrate that the proposed method can significantly improve the performance of image retrieval.