使用模糊特征编码和视觉词加权的改进BOW方法

Umit Lutfu Altintakan, A. Yazıcı
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引用次数: 4

摘要

词袋(BOW)已成为一种流行的图像表示模型,在视觉分析中得到了成功的实现。虽然对原始模型进行了多种改进,但模糊集理论在BOW中的应用还没有得到深入的研究。本文提出了一种模糊特征编码方法,以解决图像特征对视觉词的硬、软赋值问题。我们的编码方法将每个图像特征只分配给码本中最接近的第一个和第二个单词,以克服单词不确定性问题。此外,我们还引入了一种新的基于图像直方图的图像分类词加权方案。在一些图像数据集上进行的实验表明,这两种方法都提高了基于内容的图像检索的BOW性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved BOW approach using fuzzy feature encoding and visual-word weighting
The bag-of-words (BOW) has become a popular image representation model with successful implementations in visual analysis. Although the original model has been improved in several ways, the utilization of the Fuzzy Set Theory in BOW has not been investigated thoroughly. This paper presents a fuzzy feature encoding approach to address the problems associated with the hard and soft assignments of image features to the visual-words. Our encoding method assigns each image feature to only the first and second closest words in the codebook to overcome the word-uncertainty problem. Moreover, we introduce a new word-weighting scheme for image categories based on image histograms. Experiments conducted on some image datasets show that both methods increase the BOW performance in content based image retrieval.
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