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数据分析与知识发现  2018, Vol. 2 Issue (3): 49-59     https://doi.org/10.11925/infotech.2096-3467.2017.1023
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于图像特征匹配的推荐模型研究*
刘东苏, 霍辰辉()
西安电子科技大学经济与管理学院 西安 710071
Recommending Image Based on Feature Matching
Liu Dongsu, Huo Chenhui()
School of Economics and Management, Xidian University, Xi’an 710071, China
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摘要 

目的】基于LSH算法将图像匹配应用到图像推荐模型中, 与传统推荐模型结合, 提高推荐结果准确度。【方法】提取图像SIFT特征作为图像匹配标准, 改进基于p-Stable Distribution的LSH算法, 实现高维度下大量图片的搜索匹配, 最后融合现有协同过滤算法提出ICF-LSH推荐算法构建融合推荐模型, 并采用Python语言予以实现。【结果】使用不同的数据集对本文提出的算法进行验证, 实验表明改进的LSH算法对召回率和错误率都有一定的优化, 通过匹配耗时和Hash表长度可知该算法优化了内存利用和搜索匹配效率。由融合推荐模型的平均绝对误差MAE和精确度Precision可知, 相对传统的协同过滤推荐算法, 本文提出的ICF-LSH推荐算法提高了推荐结果的精准度。【局限】在提取图像特征时仅使用SIFT特征, 后续研究中可以尝试使用多种图像特征作为匹配依据, 提高匹配结果的可靠性。【结论】图像匹配算法基于LSH进行了一定改进, 提高了图像相似度匹配的效率, 此外, 本文提出的融合推荐模型能显著提升推荐效果。

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刘东苏
霍辰辉
关键词 SIFT特征LSH图像匹配推荐系统    
Abstract

[Objective] This paper developed an image recommendation model based on feature matching technique and the LSH algorithm, aiming to improve the accuracy of recommendations. [Methods] First, we extracted the image’s SIFT features as the matching criteria. Then, we modified the LSH algorithm to retrieve images in high dimensional settings. Finally, we proposed an ICF-LSH algorithm based on the collaborative filtering techniques to build fusion recommendation model. [Results] We examined the proposed algorithm with various datasets and achieved better recall and precision rates for image recommendation. [Limitations] Only used the SIFT feature to extract image features. More research is needed to explore other matching features. [Conclusions] The proposed model improves the performance of image matching and recommendation systems.

Key wordsSIFT Feature    LSH    Image Matching    Recommendation System
收稿日期: 2017-10-13      出版日期: 2018-04-03
ZTFLH:  N99 TP391  
基金资助:*本文系国家自然科学基金项目“基于可信语义Wiki的知识库构建方法与研究应用”(项目编号:71203173)和国家自然科学青年基金项目“大规模动态社交网络社团检测算法研究”(项目编号: 71401130)的研究成果之一
引用本文:   
刘东苏, 霍辰辉. 基于图像特征匹配的推荐模型研究*[J]. 数据分析与知识发现, 2018, 2(3): 49-59.
Liu Dongsu,Huo Chenhui. Recommending Image Based on Feature Matching. Data Analysis and Knowledge Discovery, 2018, 2(3): 49-59.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1023      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I3/49
  基于图像特征匹配的商品推荐模型构建流程
  图片特征点的匹配结果
  图片搜索匹配的主要工作原理
  数据集基本结构图
  Hash Table的数据结构
  推荐系统结构模型
数据集大小 召回率 错误率 匹配耗时(s) Hash表长度
2 000 0.82 1.421 0.042 12
4 000 0.90 1.337 0.069 19
6 000 0.93 1.223 0.085 34
8 000 0.96 1.069 0.126 47
  训练结果数据
  Hash表长度与召回率关系图Ⅰ
数据集大小 召回率 错误率 匹配耗时(s) Hash表长度
2 000 0.41 5.913 0.142 25
4 000 0.50 5.673 0.278 39
6 000 0.64 4.825 0.455 49
8 000 0.77 4.546 0.724 61
  不引入H1H2的训练结果数据
  Hash表长度与召回率关系图Ⅱ
  ICF算法和ICF-LSH融合推荐算法结果对比
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