Similarity‐based Retrieval With Structure‐sensitive Sparse Binary Distributed Representations

Rachkovskij, Dmitri A.; Slipchenko, S. O.

doi:10.1111/j.1467-8640.2011.00423.x

Cited by 45 publications

(30 citation statements)

References 74 publications

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“…VSAs make use of additional operations on high-dimensional vectors. So far, VSAs have been applied in various fields including robotics [45], addressing catastrophic forgetting in deep neural networks [9], medical diagnosis [73], fault detection [29], analogy mapping [52], reinforcement learning [30], long-short term memory [11], text classification [31], and synthesis of finite state automata [49]. They have been used in combination with deep-learned descriptors before, e.g.…”

Section: Vector Symbolic Architecturesmentioning

confidence: 99%

Vector Semantic Representations as Descriptors for Visual Place Recognition

Neubert¹,

Schubert²,

Schlegel³

et al. 2021

Robotics: Science and Systems XVII

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Place recognition is the task of recognizing the current scene from a database of known places. The currently dominant algorithmic paradigm is to use (deep learning based) holistic feature vectors to describe each place and use fast vector query methods to find matchings. We propose a novel type of image descriptor, Vector Semantic Representations (VSR), that encodes the spatial semantic layout from a semantic segmentation together with appearance properties in a, for example, 4,096 dimensional vector for place recognition. We leverage operations from the established class of Vector Symbolic Architectures to combine symbolic (e.g. class label) and numeric (e.g. feature map response) information in a common vector representation. We evaluate the proposed semantic descriptor on 13 standard mobile robotic place recognition datasets and compare to six descriptors from the literature. VSR is on par with the best compared descriptor (NetVLAD) in terms of mean average precision and superior in terms of recall and worst-case average precision. This makes the approach particularly interesting for candidate selection. For a more detailed investigation, we discuss and evaluate recall integrity as additional criterion. Further, we demonstrate that the semantic descriptor is particularly well suited for combination with existing appearance descriptors indicating that semantics provide complementary information for image matching.

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Section: Vector Symbolic Architecturesmentioning

confidence: 99%

Vector Semantic Representations as Descriptors for Visual Place Recognition

Neubert¹,

Schubert²,

Schlegel³

et al. 2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

show abstract

“…Note that such data representation schemes by similarity preserving binary vectors have been developed for objects represented by various data types, mainly for (feature) vectors (see survey in [131]), but also for structured data types such as sequences [102,72,85,86] and graphs [127,128,148,136,62,134]. A significant part of this research is developed in the framework of distributed representations [45,76,106,126,89], including binary sparse distributed representations [102,98,103,127,128,113,114,137,138,139,148,135,136,61,134,129,130,131,132,31,33] and dense distributed representations [75,76] (see [82,84,87,88,83] for examples of their applications).…”

Section: Generalization In Namsmentioning

confidence: 99%

Untitled

2017

Kibern. vyčisl. teh.

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Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We 5

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“…This is achieved by using codewords with very large dimensions. Several different types of VSAs have been introduced, each using different representations (Plate, 2003;Kanerva, 2009;Gayler, 1998;Rachkovskij and Kussul, 2001;Aerts et al, 2009;Rachkovskij et al, 2013;Gallant and Okaywe, 2013;Snaider and Franklin, 2014).…”

Section: Related Workmentioning

confidence: 99%

Imitation of honey bees’ concept learning processes using Vector Symbolic Architectures

Kleyko

Osipov

Gayler³

et al. 2015

Biologically Inspired Cognitive Architectures

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This article presents a proof-of-concept validation of the use of Vector Symbolic Architectures as central component of an online learning architectures. It is demonstrated that Vector Symbolic Architectures enable the structured combination of features/relations that have been detected by a perceptual circuitry and allow such relations to be applied to novel structures without requiring the massive training needed for classical neural networks that depend on trainable connections.The system is showcased through the functional imitation of concept learning in honey bees. Data from real-world experiments with honey bees (Avarguès-Weber et al., 2012) are used for benchmarking. It is demonstrated that the proposed pipeline features a similar learning curve and accuracy of generalization to that observed for the living bees. The main claim of this article is that there is a class of simple artificial systems that reproduce the learning behaviors of certain living organisms without requiring the implementation of computationally intensive cognitive architectures. Consequently, it is possible in some cases to implement rather advanced cognitive behavior using simple techniques.

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Similarity‐based Retrieval With Structure‐sensitive Sparse Binary Distributed Representations

Cited by 45 publications

References 74 publications

Vector Semantic Representations as Descriptors for Visual Place Recognition

Vector Semantic Representations as Descriptors for Visual Place Recognition

Untitled

Imitation of honey bees’ concept learning processes using Vector Symbolic Architectures

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