# Hyperdimensional computing as a framework for systematic aggregation of image descriptors arXiv: https://arxiv.org/abs/2101.07720 ยท HTML: https://mlsanity.com/paper/2101.07720/ ## Key facts - Cited by 32 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 67 references that are in the map. - First posted on arXiv 19 Jan 2021. ## Builds on (most cited first, 18 of 18) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015) - [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019) - [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016) - [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020) - [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017) - [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015) - [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019) - [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020) - [Learning and aggregating deep local descriptors for instance-level recognition](https://mlsanity.com/paper/2007.13172/) (2020) - [From Word to Sense Embeddings: A Survey on Vector Representations of Meaning](https://mlsanity.com/paper/1805.04032/) (2018) - [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016) - [Associative Long Short-Term Memory](https://mlsanity.com/paper/1602.03032/) (2016) - [A theory of sequence indexing and working memory in recurrent neural networks](https://mlsanity.com/paper/1803.00412/) (2018) - [Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset](https://mlsanity.com/paper/2002.10152/) (2020) - [RF-Net: An End-to-End Image Matching Network based on Receptive Field](https://mlsanity.com/paper/1906.00604/) (2019) - [Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments](https://mlsanity.com/paper/2001.08960/) (2020) - [REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval](https://mlsanity.com/paper/1906.06626/) (2019) ## Built on by (most cited first, 30 of 32) - [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021) - [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021) - [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021) - [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023) - [Understanding Hyperdimensional Computing for Parallel Single-Pass Learning](https://mlsanity.com/paper/2202.04805/) (2022) - [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021) - [HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing](https://mlsanity.com/paper/2202.08055/) (2022) - [Unpaired Image Translation via Vector Symbolic Architectures](https://mlsanity.com/paper/2209.02686/) (2022) - [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023) - [Hyperseed: Unsupervised Learning with Vector Symbolic Architectures](https://mlsanity.com/paper/2110.08343/) (2021) - [JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.19787/) (2024) - [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024) - [Image Labels Are All You Need for Coarse Seagrass Segmentation](https://mlsanity.com/paper/2303.00973/) (2023) - [An Encoding Framework for Binarized Images using HyperDimensional Computing](https://mlsanity.com/paper/2312.00454/) (2023) - [Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction](https://mlsanity.com/paper/2503.06840/) (2025) - [Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences](https://mlsanity.com/paper/2112.15475/) (2021) - [Laplace-HDC: Understanding the geometry of binary hyperdimensional computing](https://mlsanity.com/paper/2404.10759/) (2024) - [Vector-Symbolic Architecture for Event-Based Optical Flow](https://mlsanity.com/paper/2405.08300/) (2024) - [On Motion Blur and Deblurring in Visual Place Recognition](https://mlsanity.com/paper/2412.07751/) (2024) - [pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM](https://mlsanity.com/paper/2502.11955/) (2025) - [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025) - [Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences](https://mlsanity.com/paper/2201.11691/) (2022) - [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024) - [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024) - [On Design Choices in Similarity-Preserving Sparse Randomized Embeddings](https://mlsanity.com/paper/2501.14741/) (2024) - [Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture](https://mlsanity.com/paper/2502.14075/) (2025) - [Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation](https://mlsanity.com/paper/2506.15988/) (2025) - [Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2401.08263/) (2024) - [Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline](https://mlsanity.com/paper/2403.10283/) (2024) - [ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs](https://mlsanity.com/paper/2506.09282/) (2025) ## Abstract (arXiv) Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representation in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep-learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance compared to runner-up and 3.6x better worst-case performance.