Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey

Cao, Yuan; Qi, Heng; Zhou, Wenrui; Kato, Jien; Li, Keqiu; Gui, Jie

doi:10.1109/access.2017.2781360

Cited by 85 publications

(34 citation statements)

References 40 publications

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“…However, this kind of data-independent hashing methods needs long bits to ensure retrieval precision [18]. To overcome the mentioned limitation, data-dependent hashing becomes popular recently, which aims at learning the hash functions from the specific dataset so that the retrieval results based on the obtained hash codes are as similar as the results based on the original features [25]. In general, there are three key points in learning to hash [26], including similarity preserving, low quantization loss, and bit balance.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Tang

Liu

et al. 2019

Remote Sensing

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Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers' attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Tang

Liu

et al. 2019

Remote Sensing

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“…The broad applicability of the metric space similarity model makes the metric search a challenging task, since the distance function is the only operation that can be exploited to compare two objects. One way to speed-up the metric searching is to transform the space to use a cheaper similarity function or to reduce data object sizes [4,9,14,19]. Recently, Connor et al proposed the n-Simplex projection that transforms the metric space into a finite-dimensional Euclidean space [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Another class of metric space transformations is formed by sketching techniques that transform data objects into short bit-strings called sketches [4,17,19]. The similarity of sketches is expressed by the Hamming distance, and sketches are exploited to prune the search space during query executions [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…The similarity of sketches is expressed by the Hamming distance, and sketches are exploited to prune the search space during query executions [18,19]. While some sketching techniques are applicable in generic metric spaces, others are designed for specific spaces [4]. The metric-based sketching techniques are broadly applicable, but their performance is often worse than that of the vectorbased sketching approaches when dealing with the vector spaces [4,17].…”

Section: Introductionmentioning

confidence: 99%

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Metric Embedding into the Hamming Space with the n-Simplex Projection

Vadicamo

Mic

Falchi

et al. 2019

Similarity Search and Applications

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Transformations of data objects into the Hamming space are often exploited to speed-up the similarity search in metric spaces. Techniques applicable in generic metric spaces require expensive learning, e.g., selection of pivoting objects. However, when searching in common Euclidean space, the best performance is usually achieved by transformations specifically designed for this space. We propose a novel transformation technique that provides a good trade-off between the applicability and the quality of the space approximation. It uses the n-Simplex projection to transform metric objects into a low-dimensional Euclidean space, and then transform this space to the Hamming space. We compare our approach theoretically and experimentally with several techniques of the metric embedding into the Hamming space. We focus on the applicability, learning cost, and the quality of search space approximation.

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“…It has been also studied that there are various forms of open source frameworks e.g. Hadoop, Map Reduce, etc that are frequently studied in regards to the concept of big data approach [8]- [10], however, such frameworks are more inclined on cloud computing and less on grid computing. There is no dedicated framework or tool or model that identifies the complexities associated with data management n distributed grid environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Design of Sophisticated Distributed Knowledge Extraction Process on Grid Architecture

Parveen¹,

Narsimha²

2018

CAE

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With the rising demands of ubiquitous applications, the complexities associated with the data are exponentially increasing. Although, such massively generated complex data doesn't pose much challenge in storage system, but it definitely strikes a challenging problem in order to perform mining. The process of discovering the valuable knowledge becomes much challenging if a distributed architecture of grid network is considered. Therefore, the proposed system introduces a novel architecture that is capable of performing error-free distributed mining over grid networks. The significant contribution of proposed system is to apply a novel and cost effective optimization technique for simplifying the data structurization problem in distributed system that is found to normalize the existing data complexity problems. The study outcome exhibits significantly low errors and minimal computational cost in presence of peak traffic condition to prove that proposed architecture offers better mining approach in contrast to existing approaches.

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Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey

Cited by 85 publications

References 40 publications

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Metric Embedding into the Hamming Space with the n-Simplex Projection

A Novel Design of Sophisticated Distributed Knowledge Extraction Process on Grid Architecture

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