Visual manifold sensing

Burciu, Irina; Ion-Mărgineanu, Adrian; Martinetz, Thomas; Barth, Erhardt

doi:10.1117/12.2043012

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…They also Figure 8. Selected HMS sensing basis functions without foveation (second to fifth column-for L = 1, 3,7,8,9) and the corresponding sample test image (first column) from the MNIST dataset. For the hierarchical partitioning we used k = 2 clusters and N L = 9 for L = 1.…”

Section: Performance Of Hms Versus Other Methodsmentioning

confidence: 99%

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Hierarchical Manifold Sensing with Foveation and Adaptive Partitioning of the Dataset

Burciu¹,

Martinetz²,

Barth³

2016

jist

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The authors present a novel method, Hierarchical Manifold Sensing, for adaptive and efficient visual sensing. As opposed to the previously introduced Manifold Sensing algorithm, the new version introduces a way of learning a hierarchical partitioning of the dataset based on k-means clustering. The algorithm can perform on whole images but also on a foveated dataset, where only salient regions are sensed. The authors evaluate the proposed algorithms on the COIL, ALOI, and MNIST datasets. Although they use a very simple nearest-neighbor classifier, on the easier benchmarks, COIL and ALOI, perfect recognition is possible with only six or ten sensing values. Moreover, they show that their sensing scheme yields a better recognition performance than compressive sensing with random projections. On MNIST, state-of-the-art performance cannot be reached, but they show that a large number of test images can be recognized with only very few sensing values. However, for many applications, performance on challenging benchmarks may be less relevant than the simplicity of the solution (processing power, bandwidth) when solving a less challenging problem.

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Section: Performance Of Hms Versus Other Methodsmentioning

confidence: 99%

“…The Manifold Sensing concept was introduced before by Burciu et al as visual Manifold Sensing (MS), 7 and it was extended afterwards to the foveated version of Manifold Sensing (FMS), 8 which was inspired by the sampling strategy of biological systems. Like Manifold Sensing, HMS is based on a geometric approach.…”

Section: Introductionmentioning

confidence: 99%