Human Image Complexity Analysis Using a Fuzzy Inference System

Madrid-Herrera, Luis; Chacón-Murguía, Mario I.; Posada-Urrutia, Daniel A.; Ramírez-Quintana, Juan A.

doi:10.1109/fuzz-ieee.2019.8858966

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In fact, Shannon entropy reveals little about an image since a shuffle of the image pixels yields the same classical value for Shannon entropy. While other entropy measures have been proposed as a proxy for image complexity [3], creating a generalizable image complexity measure remains a difficult problem [4]. Recently, with optical neural networks and Fourierdomain inputs, we have shown that the choice of generalized training images leads to learned preferences that distil different salient features of the reconstructed images based on spectral correlations [5].…”

Section: Introductionmentioning

confidence: 99%

Feature and texture distillation via neural network training

Perry,

Weng,

Feng

et al. 2023

EPJ Web Conf.

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Section: Introductionmentioning

confidence: 99%

Feature and texture distillation via neural network training

Perry,

Weng,

Feng

et al. 2023

EPJ Web Conf.

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AENCIC: a method to estimate the number of clusters based on image complexity to be used in fuzzy clustering algorithms for image segmentation

2023

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Recognition of Bangla Handwritten Number Using Combination of PCA and FIS with the Aid of DWT

Khandakar¹,

Islam²,

Tabassum³

et al. 2020

JCC

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The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: "one and nine" and "five and six". We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.

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Human Image Complexity Analysis Using a Fuzzy Inference System

Cited by 3 publications

References 24 publications

Feature and texture distillation via neural network training

Feature and texture distillation via neural network training

AENCIC: a method to estimate the number of clusters based on image complexity to be used in fuzzy clustering algorithms for image segmentation

Recognition of Bangla Handwritten Number Using Combination of PCA and FIS with the Aid of DWT

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