Experimental Analysis of Locality Sensitive Hashing Techniques for High-Dimensional Approximate Nearest Neighbor Searches

Jafari, Omid; Nagarkar, Parth

doi:10.1007/978-3-030-69377-0_6

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…This operation results in less disk I/Os since it prevents reading unnecessary buckets from the disk. Nevertheless, as we show in our experimental paper [49], this incremental strategy leads to high computation costs. In [74], authors extend I-LSH and introduce EI-LSH that features an aggressive early termination condition in order to stop the algorithm when good enough candidates are found and save the processing time.…”

Section: Minkowski-based Lsh Techniquesmentioning

confidence: 81%

A Survey on Locality Sensitive Hashing Algorithms and their Applications

Jafari¹,

Maurya²,

Nagarkar³

et al. 2021

Preprint

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Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guarantees on the query accuracy. In this survey paper, we provide a review of state-of-the-art LSH and Distributed LSH techniques. Most importantly, unlike any other prior survey, we present how Locality Sensitive Hashing is utilized in different application domains.CCS Concepts: • General and reference → Surveys and overviews.

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Section: Minkowski-based Lsh Techniquesmentioning

confidence: 81%

A Survey on Locality Sensitive Hashing Algorithms and their Applications

Jafari¹,

Maurya²,

Nagarkar³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In future work, we will explore learning and task execution with sparse activation to determine if there are critical constraints on proportion active and overlap to enable learning of the task. It is interesting in this context to note that there are several algorithms that interpret the input circuits of the MB as locality-sensitive hashing [71] by which high-dimensional input can be mapped into the state represented as a sparse number of active KCs.…”

Section: Multi-node Activationmentioning

confidence: 99%

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Wei,

Guo,

Webb

2024

PLoS Comput Biol

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Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This ‘distal’ or ‘sparse’ reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

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“…The original high-dimensional space is mapped to the projected lowdimensional space using random hash projections in LSH. There are two key benefits of LSH: its sub-linear query performance (in terms of the data quantity) and the theoretical assurances of query accuracy [29]. The LSH family H is a probability P distribution on a family H of hash functions h, where the similarity function S is established on the set of objects X and Y [30,31].…”

Section: Locality-sensitive Hashing Techniquementioning

confidence: 99%

Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

Alshahrani

Jaha

2023

Electronics

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As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash).

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Experimental Analysis of Locality Sensitive Hashing Techniques for High-Dimensional Approximate Nearest Neighbor Searches

Cited by 12 publications

References 15 publications

A Survey on Locality Sensitive Hashing Algorithms and their Applications

A Survey on Locality Sensitive Hashing Algorithms and their Applications

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

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