Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

Tunable multi-part perceptual image hashing

Abstract: Systems and methods generate a perceptual image hash of an image. The perceptual image hash can be generated from multiple features extracted from a DCT transformation of the image. The perceptual image hash can be compared to other perceptual image hash values using a weighted Hamming distance function.

Method and system for entropy-based semantic hashing

Abstract: Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.

DCT based multiple hashing technique for robust audio fingerprinting

Abstract: Audio fingerprinting techniques should successfully perform content-based audio identification even when the audio files are slightly or seriously distorted. In this paper, we present a novel audio fingerprinting technique based on combining fingerprint matching results for multiple hash tables in order to improve the robustness of hashing. Multiple hash tables are built based on the discrete cosine transform (DCT) which is applied to the time sequence of energies in each sub-band. Experimental results show that the recognition errors are significantly reduced compared with Philips Robust Hash (PRH) [1] under various distortions.

Data storage method and apparatus utilizing evolution and hashing

Abstract: Hashing functions have many practical applications in data storage and retrieval. Perfect hashing functions are extremely difficult to find, especially if the data set is large and without large-scale structure. There are great rewards for finding good hashing functions, considering the savings in computational time such functions provide, and much effort has been expended in this search. This in mind, we present a strong competitive evolutionary method to locate efficient hashing functions for specific data sets by sampling and evolving from the set of polynomials over the ring of integers mod n. We find favorable results that seem to indicate the power and usefulness of evolutionary methods in this search. Polynomials thus generated are found to have consistently better collision frequencies than other hashing methods. This results in a reduction in average number of array probes per data element hashed by a factor of two. Presented herein is an evolutionary algorithm to locate efficient hashing functions for specific data sets. Polynomials are used to investigate and evaluate various evolutionary strategies. Populations of random polynomials are generated, and then selection and mutation serve to eliminate unfit polynomials. The results are favorable and indicate the power and usefulness of evolutionary methods in hashing. The average number of collisions using the algorithm presented herein is about one-half of the number of collisions using other hashing methods. Efficient methods of data storage and retrieval are essential to today's information economy. Despite the cur-rent obstacles to creating efficient hashing functions, hashing is widely used due to its efficient data access. This study investigates the feasibility of overcoming such obstacles through the application of Darwin's ideas by modeling the basic principles of biological evolution in a computer. Polynomials over Zn are the evolutionary units and it is believed that competition and selection based on performance would locate polynomials that make efficient hashing functions.