基于图像特征匹配的推荐模型研究

Recommending Image Based on Feature Matching

【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。

[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.