Efficient set representation and membership testing are important in various big data applications. The state-of-the-art Cuckoo filter design shows great advantages in both query efficiency and the support of item deletion, compared to previous Bloom filter and its variants. However, in this work, we show mathematically and experimentally that Cuckoo filter may suffer serious performance degradation during element insertion because of its random choice strategy for inserting an element into the candidate buckets. Such a random choice strategy incurs load imbalance among different buckets in Cuckoo filter and can lead to frequent relocations and the consequent long time for inserting an item. To solve this problem, we propose a novel design which leverages the principle of the power of two choices to select the better candidate bucket during inserting an element. Our design balances the load distribution among buckets in Cuckoo filter and avoids a large amount of relocations during insertion. We implement our design and apply it in real-world applications. We conduct comprehensive experiments using large-scale data sets collected from real world systems to evaluate the performance of this design. The results show that our design significantly reduces the average number of relocations of Cuckoo filter by 35%, as well as reducing item inserting latency by 25%.

Almost all conventional template-matching methods employ low-level image features to measure the similarity between a template image and a scene image using similarity measures such as pixel intensity and pixel gradient. Although these methods have been widely used in many applications, they cannot simultaneously address all types of robustness challenges. In this study, with the goal of simultaneously addressing the various challenges, we present a robust semantic template-matching approach (RSTM). Inspired by the local binary descriptor, we propose a novel superpixel region binary descriptor (SRBD) to construct a multilevel semantic fusion feature vector for RSTM. SRBD uses a new kernel-distance-based simple linear iterative clustering (KD-SLIC) method to extract the stable superpixels from the template image; Then, based on the average intensity difference between each superpixel region and its neighbors, the dominant gradient orientation of each superpixel can be obtained, and the semantic features of each superpixel can be described as the dominant orientation difference vector, which is coded as the rotation-invariant SRBD. In the off-line matching phase, the fusion semantic feature vector of RSTM combines the multilevel SRBD features with different numbers of superpixels. In the online matching phase, to cope with rotation invariance, a marginal probability model is proposed and applied to locate the positions of template images in the scene image. Moreover, to accelerate computation, an image pyramid is employed. We conduct a series of experiments on a large dataset randomly selected from the MS COCO dataset to fully analyze the robustness of this approach. The experimental results show that RSTM simultaneously addresses rotation changes, scale changes, noise, occlusions, blur, nonlinear illumination changes and deformation with high time efficiency while also outperforming previous stateof- the-art template-matching methods.