Memorability‐based image compression

Khanna, Meera Thapar; Ralekar, Chetan; Goel, Anurika; Chaudhury, Santanu; Lall, Brejesh

doi:10.1049/iet-ipr.2018.6097

Cited by 5 publications

(2 citation statements)

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“…Lower-dimensional representations can improve performance on many tasks, such as image compression, reconstruction and clustering (Liu et al, 2019). Color image compression occurs in many information retrieval problems (Dewi et al, 2021;Khanna et al, 2019;Knop et al, 2016). In such tasks, org/10.15405/epct.23021.36 Corresponding Author: V. V. Matskevich Selection and peer-review under responsibility of the Organizing Committee of the conference eISSN: 2672-8834 the restricted Boltzmann machine (RBM) is often used, which allows you to extract informative features that are used later for image classification.…”

Section: The Problem Of Color Images Compression Can Be Formally Writ...mentioning

confidence: 99%

An Efficient Training Algorithm of Restricted Boltzmann Machines

Matskevich¹

2023

European Proceedings of Computers and Technology

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The paper deals with an actual applied problem related to the artificial neural networks training. An approach to the solution based on the idea of random search is proposed. An original training algorithm that implements Boltzmann annealing has been developed and its convergence in probability to the global optimum has been proved. It is also shown that the proposed algorithm can be easily modified to train any artificial neural network. Thus, it has a good prospect for solving applied problems using neural network technologies in general. Experimental studies have been carried out, in which, using the example of compressing color raster images problem, the proposed algorithm was compared with the known adaptive moment algorithm -one of the best gradient methods for training neural networks. Image compression was performed using an ensemble of n Gauss-Bernoulli restricted Boltzmann machines. The use of an ensemble of n machines in combination with a specially developed parallelization procedure made it possible to reduce the computational complexity of the training process and increase the speed of the proposed algorithm. As a result of experiments, it was shown that the proposed approach is not inferior to gradient methods in terms of speed. Moreover, the developed training algorithm turned out to be more than twice as effective as the adaptive moment algorithm in terms of the quality of the solution obtained.

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Section: The Problem Of Color Images Compression Can Be Formally Writ...mentioning

confidence: 99%

An Efficient Training Algorithm of Restricted Boltzmann Machines

Matskevich¹

2023

European Proceedings of Computers and Technology

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“…The eye movements can be considered to provide a valid measure of the spatial distribution of attention on account of the tight coupling between attention and eye movements in natural viewing tasks [12], [13]. Hence, eye-tracking has been used in various applications [14]- [18]. We are using eye-tracking methodology to unlock the mechanism of character recognition.…”

Section: A Eye-tracking Experimentsmentioning

confidence: 99%

Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks

Ralekar,

Choudhary,

Gandhi

et al. 2021

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Human observers engage in selective information uptake when classifying visual patterns. The same is true of deep neural networks, which currently constitute the best performing artificial vision systems. Our goal is to examine the congruence, or lack thereof, in the information-gathering strategies of the two systems. We have operationalized our investigation as a character recognition task. We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps. Qualitative comparison between visualization maps and fixation maps reveals an interesting correlate of congruence. The deep learning model considered similar regions in character, which humans have fixated in the case of correctly classified characters. On the other hand, when the focused regions are different for humans and deep nets, the characters are typically misclassified by the latter. Hence, we propose to use the visual fixation maps obtained from the eye-tracking experiment as a supervisory input to align the model's focus on relevant character regions. We find that such supervision improves the model's performance significantly and does not require any additional parameters. This approach has the potential to find applications in diverse domains such as medical analysis and surveillance in which explainability helps to determine system fidelity.

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