基于多神经散列码和Bloom滤波器的高效图像检索
Efficient image retrieval using multi neural hash codes and bloom filters
摘要：
本文的目的是提供一个有效的和修改的方法，图像检索使用多个神经哈希码和限制查询使用布隆过滤器的数量，通过识别误报事先。涉及用于图像检索任务的神经网络的传统方法倾向于使用更高层来进行特征提取。但是已经看到，较低层的激活在许多情况下被证明是更有效的。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，该网络结合了较低层和较高层的特征的能力，用于创建特征图，然后使用PCA压缩特征图，并在使用修改的多k均值方法进行二进制排序后将其馈送到布隆过滤器。所获得的特征图进一步用于图像检索过程中，通过首先比较语义相似图像的较高层中的图像，然后逐渐向较低层移动以搜索结构相似性，以分级的从粗到细的方式。在搜索时，再次计算查询图像的神经哈希，并在布隆过滤器中查询，布隆过滤器告诉我们查询图像是否存在于集合中。如果布隆过滤器不一定排除查询，那么它进入图像检索过程。这种方法在图像存储是分布式的情况下特别有用，因为这种方法支持并行查询。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
