【摘要】 针对移动增强现实中图像检索技术耗时长导致的实时性不高的问题,提出了一种基于感知哈希和视觉词袋模型结合的图像检索方法。图像检索过程中,在保证一定正确率的基础上加快了检索速度。首先,对数据集图像使用改进的感知哈希技术处理,选取与查询相似的图像集合,达到筛选图像数据集的作用;然后,对相似图像集使用视觉词袋模型进行图像检索,选取和查询图像中目标一致的目标图像。实验结果表明,该方法相比较视觉词袋模型算法检索的平均正确率提高了3.2%,检索时间缩短了102.9 ms,能够满足移动增强现实中图像检索的实时性要求,为移动增强现实系统提供了有利的条件。更多还原

【Abstract】 As the existing image retrieval technologies in mobile augmented reality have a low real-time performance caused by long time-consuming, this paper proposes a novel image retrieval method which combines the perceptual hashing and bag of visual word model(BoVW). The method is able to accelerate the search speed with certain accuracy. First, the improved perceptual hashing is used to retrieve a image set in which each image is similar to the current image, which limits the scope of the target. Then a BoVW model is built based on this image set, the BoVW model is used to create a visual vector for each image in the image set and the current image. Finally, hamming distance of the visual vector between the current image and each image in the image set is calculated to finish the image retrieval. The results show that the improvement of our method in accuracy is 3.2% and the retrieval time is reduced by 102.9 ms to the traditional BoVW model algorithm. Our method is able to meet the real-time requirements of image retrieval in mobile augmented reality.更多还原