High-performance content-based image retrieval using DFS strategy

Ja-Hwung Su, Chung-Chieh Hsu, J. Ying
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引用次数: 1

Abstract

Image data is becoming more and more popular due to the prevalence of image capture devices. How to retrieve the images effectively and efficiently from a large number of images has been a challenging issue in recent years. To deal with such issue, the major purpose of this paper is to propose a concept- and content-aware image retrieval approach using Depth-First Search (DFS) strategy to conduct effective and efficient image semantic retrieval. For effectiveness and efficiency, since the search space is reduced into specific subspaces, the retrieval cost is decreased and the retrieval quality is increased. For semantic retrieval, our proposed method can detect the potential concepts to satisfy the user's semantic need. In the experimental result, it reveals that our proposed approach is more effective and efficient than traditional ones using Breadth-First-Search (BFS) strategy.
使用DFS策略的高性能基于内容的图像检索
由于图像捕获设备的普及,图像数据变得越来越流行。如何从海量图像中高效地检索图像是近年来一个具有挑战性的问题。为了解决这一问题,本文的主要目的是提出一种概念感知和内容感知的图像检索方法,利用深度优先搜索(deep - first Search, DFS)策略进行有效的图像语义检索。为了提高检索的有效性和效率,由于将检索空间简化为特定的子空间,降低了检索成本,提高了检索质量。在语义检索方面,我们提出的方法可以检测潜在的概念以满足用户的语义需求。实验结果表明,该方法比传统的宽度优先搜索(BFS)策略更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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