Similarity Retrieval Based on Image Background Analysis

Chang Zhu, Wenchao Jiang, Weilin Zhou, Hong Xiao
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Abstract

Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.
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基于图像背景分析的相似度检索
针对传统人像背景相似度检索方法准确率低、耗时长的问题,提出了一种基于图像背景分析的人像背景相似度检索方法。该方法采用图像分割和图像检索相结合的方法。首先,利用人像分割模型去除图像中的人像,消除人像对背景特征的干扰;其次,利用图像检索模型对具有相似背景特征的图像进行检索;加入LSH,提高检索效率;最后,利用检索结果进一步判断背景是否相似。实验以某公司的实际数据为基础进行。结果表明,该方法的平均精密度达到85%,平均图谱达到90%,召回率达到50%。平均准确率和召回率比整体图像检索模型提高10%。
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