Topic
Feature hashing
About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.
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01 Jan 2010
TL;DR: It is demonstrated how easy it is to get started using the powerful features of the hash object and examples of how these features can improve programmer productivity and system performance are shown.
Abstract: The SAS® hash object has come of age in SAS 9.2, giving the SAS programmer the ability to quickly do things never before possible within a single data step. This paper will demonstrate how easy it is to get started using the powerful features of the hash object and show examples of how these features can improve programmer productivity and system performance.
2 citations
TL;DR: The paper presents a technique to enhance the search performance by introducing the notion of extended boundary, which reduces the potential misses and the search overhead especially for the regions located at the double-napped corners.
Abstract: Locality-sensitive hashing is a technique to allow approximate nearest search for large volume of data in a fast manner. Binary code locality-sensitive hashing distributes a data set into buckets labeled with binary code, where binary codes are determined by a set of hash functions. The binary hash codes play the role of partitioning the data space into subspaces. When close neighbors are placed around subspace boundaries, there are chances to fail in locating them. It requires to check neighboring buckets while finding nearest ones. The paper presents a technique to enhance the search performance by introducing the notion of extended boundary. It reduces the potential misses and the search overhead especially for the regions located at the double-napped corners. Keywords: locality sensitive hashing, data search, hashing, data analysis
2 citations
01 Dec 2007
TL;DR: An image hashing technique that attempts to simultaneously address the robustness, fragility and security issues is presented and an improved version of this scheme with a wavelet-based smoothening to improve robustness against JPEG compression and a modified intensity-transformation for enhancing the security.
Abstract: Designing a hash function for multimedia authentication encompasses many issues like robustness to non- malicious distortion, sensitivity to detect malicious manipulations and security In this paper, we present an image hashing technique that attempts to simultaneously address the robustness, fragility and security issues This scheme is an improved version of our previously proposed scheme [1] with a wavelet-based smoothening to improve robustness against JPEG compression and a modified intensity-transformation for enhancing the security Several experimental results are presented to demonstrate the effectiveness of the proposed scheme
2 citations
Journal Article•
TL;DR: A robust 3D content-based hashing based on key-dependent 3D surface feature that has the robustness against geometry and topology attacks and has the uniqueness of hash in each model and key is developed.
Abstract: With the rapid growth of 3D content industry fields, 3D content-based hashing (or hash function) has been required to apply to authentication, trust and retrieval of 3D content. A content hash can be a random variable for compact representation of content. But 3D content-based hashing has been not researched yet, compared with 2D content-based hashing such as image and video. This paper develops a robust 3D content-based hashing based on key-dependent 3D surface feature. The proposed hashing uses the block surface coefficient using shape coordinate of 3D SSD and curvedness for 3D surface feature and generates a binary hash by a permutation key and a random key. Experimental results verified that the proposed hashing has the robustness against geometry and topology attacks and has the uniqueness of hash in each model and key.
2 citations
04 Jan 2016
TL;DR: This paper proposes a self-organizing map based hashing method---SOMH, which cannot only keep similarity relationship, but also preserve topology of data, and proposes a relaxed version of SOMH and a product space SOMH, respectively.
Abstract: Hashing based approximate nearest neighbor search techniques have attracted considerable attention in media search community. An essential problem of hashing is to keep the neighborhood relationship while doing hashing map. In this paper, we propose a self-organizing map based hashing method---SOMH, which cannot only keep similarity relationship, but also preserve topology of data. Specifically, in SOMH, self-organizing map is introduced to map data points into hamming space. In this framework, in order to make it work well on short and long binary codes, we propose a relaxed version of SOMH and a product space SOMH, respectively. For the optimization problem of relaxed SOMH, we also present an iterative solution. To test the performance of SOMH, we conduct experiments on two benchmark datasets---SIFT1M and GIST1M. Experimental results show that SOMH can outperform or is comparable to several state-of-the-arts.
2 citations