计算机应用 ›› 2017, Vol. 37 ›› Issue (8): 2244-2247.DOI: 10.11772/j.issn.1001-9081.2017.08.2244

• 人工智能 • 上一篇    下一篇

基于优化视觉词袋模型的图像分类方法

张永, 杨浩   

  1. 兰州理工大学 计算机与通信学院, 兰州 730050
  • 收稿日期:2016-12-13 修回日期:2017-03-11 出版日期:2017-08-10 发布日期:2017-08-12
  • 通讯作者: 杨浩
  • 作者简介:张永(1963-),男,甘肃兰州人,教授,主要研究方向:智能信息处理、数据挖掘;杨浩(1991-),男,甘肃陇南人,硕士研究生,主要研究方向:图像分类、机器学习。

Image classification method based on optimized bag-of-visual words model

ZHANG Yong, YANG Hao   

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2016-12-13 Revised:2017-03-11 Online:2017-08-10 Published:2017-08-12

摘要: 针对视觉词袋(BOV)模型中过大的视觉词典会导致图像分类时间代价过大的问题,提出一种加权最大相关最小相似(W-MR-MS)视觉词典优化准则。首先,提取图像的尺度不变特征转换(SIFT)特征,并用K-Means算法对特征聚类生成原始视觉词典;然后,分别计算视觉单词与图像类别间的相关性,以及各视觉单词间的语义相似性,引入一个加权系数权衡两者对图像分类的重要程度;最后,基于权衡结果,删除视觉词典中与图像类别相关性弱、与视觉单词间语义相似性大的视觉单词,从而达到优化视觉词典的目的。实验结果表明,在视觉词典规模相同的情况下,所提方法的图像分类精度比传统基于K-Means算法的图像分类精度提高了5.30%;当图像分类精度相同的情况下,所提方法的时间代价比传统K-Means算法下的时间代价降低了32.18%,因此,所提方法具有较高的分类效率,适用于图像分类。

关键词: 图像分类, 视觉词袋模型, 特征提取, 视觉词典

Abstract: Concerning the problem that too large visual dictionary may increase the time cost of image classification in the Bag-Of-Visual words (BOV) model, a Weighted-Maximal Relevance-Minimal Semantic similarity (W-MR-MS) criterion was proposed to optimize visual dictionary. Firstly, the Scale Invariant Feature Transform (SIFT) features of images were extracted, and the K-Means algorithm was used to generate an original visual dictionary. Secondly, the correlation between visual words and image categories and semantic similarity among visual words were calculated, and a weighted parameter was introduced to measure the importance of the correlation and the semantic similarity in image classification. Finally, based on the weighing result, the visual word which correlation with image categories was weak and semantic similarity among visual words was high was removed, which achieved the purpose of optimizing the visual dictionary. The experimental results show that the classification precision of the proposed method is 5.30% higher than that of the traditional K-Means algorithm under the same visual dictionary scale; the time cost of the proposed method is reduced by 32.18% compared with the traditional K-Means algorithm under the same classification precision. Therefore, the proposed method has high classification efficiency and it is suitable for image classification.

Key words: image classification, Bag-Of-Visual words (BOV) model, feature extraction, visual dictionary

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