Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision

Zaeemzadeh, Alireza; Joneidi, Mohsen; Rahnavard, Nazanin; Shah, Mubarak

doi:10.1109/cvpr.2019.00556

Cited by 14 publications

(29 citation statements)

References 31 publications

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“…Projection of all data onto the subspace spanned by K columns of A, indexed by S, i.e., π S (A), can be expressed by a rank-K factorization, U V T . In this factorization, U ∈ R N ×K , V ∈ R M ×K , and U includes a set of K normalized columns of A, indexed by S. Therefore, the optimization problem (1) can be restated as [17]:…”

Section: Spectrum Pursuit (Sp)mentioning

confidence: 99%

“…As mentioned before, this is an NP hard problem. Recently, IPM [17], a fast suboptimal and greedy approach to tackle (3), was proposed. In IPM, samples are iteratively selected in a greedy manner until K samples are collected.…”

Section: Spectrum Pursuit (Sp)mentioning

confidence: 99%

“…Recently, a low-complexity approach was proposed to solve CSSP, referred to as iterative projection and matching (IPM) [17]. IPM is a greedy algorithm that selects K consecutive and locally optimum samples, without the op-tion of revisiting the previous selections and escaping local optima.…”

Section: Introductionmentioning

confidence: 99%

“…The projection error is normalized by projection error of random selection. The proposed SP algorithm is compared with IPM[17]…”

mentioning

confidence: 99%

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Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Joneidi¹,

Vahidian²,

Esmaeili³

et al. 2020

Preprint

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Finding a small subset of data whose linear combination spans other data points, also called column subset selection problem (CSSP), is an important open problem in computer science with many applications in computer vision and deep learning. There are some studies that solve CSSP in a polynomial time complexity w.r.t. the size of the original dataset. A simple and efficient selection algorithm with a linear complexity order, referred to as spectrum pursuit (SP), is proposed that pursuits spectral components of the dataset using available sample points. The proposed non-greedy algorithm aims to iteratively find K data samples whose span is close to that of the first K spectral components of entire data. SP has no parameter to be fine tuned and this desirable property makes it problem-independent. The simplicity of SP enables us to extend the underlying linear model to more complex models such as nonlinear manifolds and graph-based models. The nonlinear extension of SP is introduced as kernel-SP (KSP). The superiority of the proposed algorithms is demonstrated in a wide range of applications.

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Section: Spectrum Pursuit (Sp)mentioning

confidence: 99%

Section: Spectrum Pursuit (Sp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The projection error is normalized by projection error of random selection. The proposed SP algorithm is compared with IPM[17]…”

mentioning

confidence: 99%

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Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Joneidi¹,

Vahidian²,

Esmaeili³

et al. 2020

Preprint

Self Cite

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show abstract

“…As shown, SP outperforms all other methods. There is also a considerable performance gap between SP and IPM [17], the second best algorithm.…”

Section: Training Ganmentioning

confidence: 99%

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Joneidi¹,

Vahidian²,

Esmaeili³

et al. 2020

Preprint

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Representative Image Selection for Data Efficient Word Spotting

Westphal

Grahn

Lavesson

2020

Lecture Notes in Computer Science

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This paper compares three different word image representations as base for label free sample selection for word spotting in historical handwritten documents. These representations are a temporal pyramid representation based on pixel counts, a graph based representation, and a pyramidal histogram of characters (PHOC) representation predicted by a PHOCNet trained on synthetic data. We show that the PHOC representation can help to reduce the amount of required training samples by up to 69% depending on the dataset, if it is learned iteratively in an active learning like fashion. While this works for larger datasets containing about 1 700 images, for smaller datasets with 100 images, we find that the temporal pyramid and the graph representation perform better.

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Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision

Cited by 14 publications

References 31 publications

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Representative Image Selection for Data Efficient Word Spotting

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