论文标题
使用多神经哈希码和盛开过滤器的有效图像检索
Efficient image retrieval using multi neural hash codes and bloom filters
论文作者
论文摘要
本文旨在通过多个神经哈希码提供一种有效而修改的方法，用于图像检索，并通过事先识别误报来使用Bloom过滤器限制查询数量。涉及图像检索任务的神经网络的传统方法倾向于使用较高的层进行特征提取。但是已经可以看出，在许多情况下，下层的激活已被证明更有效。在我们的方法中，我们利用了局部深度卷积神经网络的使用，这些卷积神经网络结合了较低和较高层的特征的功能来创建特征图，然后使用PCA压缩，并在使用修改后的Multi K-Means方法进行二进制测序后将其馈送到Bloom滤波器。通过首先比较较高的图像中的图像以在语义上相似的图像中比较较高层中的图像，然后逐渐朝着寻找结构相似性的较低层移动，以层次的粗到修改方式进一步使用了所获得的特征图。在搜索时，再次计算出查询图像的神经哈希在Bloom滤波器中再次计算并查询，该滤波器告诉我们集合中是否不存在查询图像。如果Bloom过滤器不一定排除查询，则将进入图像检索过程。在分发图像存储的情况下，由于方法支持并行查询，这种方法可能特别有用。
This paper aims to deliver an efficient and modified approach for image retrieval using multiple neural hash codes and limiting the number of queries using bloom filters by identifying false positives beforehand. Traditional approaches involving neural networks for image retrieval tasks tend to use higher layers for feature extraction. But it has been seen that the activations of lower layers have proven to be more effective in a number of scenarios. In our approach, we have leveraged the use of local deep convolutional neural networks which combines the powers of both the features of lower and higher layers for creating feature maps which are then compressed using PCA and fed to a bloom filter after binary sequencing using a modified multi k-means approach. The feature maps obtained are further used in the image retrieval process in a hierarchical coarse-to-fine manner by first comparing the images in the higher layers for semantically similar images and then gradually moving towards the lower layers searching for structural similarities. While searching, the neural hashes for the query image are again calculated and queried in the bloom filter which tells us whether the query image is absent in the set or maybe present. If the bloom filter doesn't necessarily rule out the query, then it goes into the image retrieval process. This approach can be particularly helpful in cases where the image store is distributed since the approach supports parallel querying.
