Image Retrieval Method Based on Perceptual Hash Algorithm and Bag of Visual Words

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.