上下文特征发现和图像排序,用于图像对象检索和标记细化

M. Joseph, M. S. Godwin Premi
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引用次数: 6

摘要

图像检索正在成为从大型数据库中浏览和检索图像的重要研究领域之一。目前有许多图像检索技术,但由于光照条件、噪声标签等原因,其精度较低。我们在图像检索中看到的最重要的问题之一是语义缺口。本文提出了一种利用辅助视觉词和标记细化的方法来提高图像检索系统的性能,减少语义差距。通过上下文特征的发现,提高了检索的准确性。本文还提出了一种基于对比度有限自适应直方图均衡化算法和图像排序方法的图像预处理技术。介绍了一种有效的距离度量方法和检索的最小距离分类方法。实验结果表明,该方法提高了定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contextual feature discovery and image ranking for image object retrieval and Tag refinement
Image retrieval is emerging as one of the important research area for browsing and retrieving images from a large database. Many image retrieval techniques are there but it suffers from low precision rates due to lighting conditions, noisy tags etc. One of the most important problems that we have seen in image retrieval is the semantic gap. This paper proposes a new method for improving the performance of image retrieval system and reducing the semantic gap using Auxiliary Visual Word and Tag refinement. Here the retrieval accuracy gets improved by the discovery of contextual features. This paper also provides an image pre-processing technique using Contrast Limited Adaptive Histogram Equalization algorithm and image ranking approach. It also describes about effective and efficient distance measure and a minimum distance classification for retrieval. The experimental results show that precision has been improved with the proposed method.
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