Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode the images as a vector of hand-crafted features followed by a separate binarization step to generate hash codes. This two-stage process may produce sub-optimal encoding. In this paper, for the first time, we propose a ...

With double hashing, for a key x, one generates two hash values f(x) and g(x), and then uses combinations (f(x) + ig(x)) mod n for i = 0, 1, 2, . . . to generate multiple hash values in the range [0, n− 1] from the initial two. For balanced allocations, keys are hashed into a hash table where each bucket can hold multiple keys, and each key is placed in the least loaded of d choices. It has bee...

Packet classification remains an important aspect of network processing as it encompasses increasingly more functionality due to newly introduced services. Essentially, it entails the matching of incoming packets against a database of rules performing the operation that is associated with the matching rule with the highest priority. Within the hashingbased packet classification algorithms, the ...

We address the problem of image hashing by learning binary codes from large and weakly supervised photo collections. Due to the explosive growth of usergenerated media on the Web, this problem is becoming critical for large-scale visual applications like image retrieval. While most existing hashing methods fail to address this challenge well, our method shows promising improvement due to the fo...

The query complexity of locality sensitive hashing (LSH) based similarity search is dominated by the number of hash evaluations, and this number grows with the data size (Indyk & Motwani, 1998). In industrial applications such as search where the data are often high-dimensional and binary (e.g., text n-grams), minwise hashing is widely adopted, which requires applying a large number of permutat...

Hashing approach becomes popular for fast similarity search in many large scale applications. Real world data are usually with multiple modalities or having different representations from multiple sources. Various hashing methods have been proposed to generate compact binary codes from multi-modal data. However, most existing multimodal hashing techniques assume that each data example appears i...

The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global representations, e.g., GIST [3], which lack the analysis of the intrinsic geometric property of local features and heavily limit the effectiveness of the hash code. In t...

We describe a construction of almost universal hash functions suitable for very fast software implementation and applicable to the hashing of variable size data and fast cryptographic message authenti-cation. Our construction uses fast single precision arithmetic which is increasingly supported by modern processors due to the growing needs for fast arithmetic posed by multimedia applications. W...

This paper investigates the performance of hashing algorithms by both an experimental and an analytical approach. We examine the performance of three classical hashing algorithms: chaining, double hashing and linear probing. Our experimental results show that, despite the theoretical superiority of chaining and double hashing, linear probing outperforms both for random lookups. We explore varia...

The hashing methods have attracted much attention for large scale image retrieval. Some deep hashing methods have achieved promising results by taking advantage of the better representation power of deep networks recently. However, existing deep hashing methods treat all hash bits equally. On one hand, a large number of images share the same distance to a query image because of the discrete Ham...