Application of Chaos in Image Processing and Recognition

Yu, Wanbo

doi:10.1109/iccsec.2017.8446823

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…Examples of chaos-based tools for image recognition have been reported in the literature. For instance, in [44] the authors approximate the digital image by a parametric Bezier function and carry out an analysis of the chaotic behavior resulting from repetitively iterating that function. In [43], a chaotic iteration procedure is proposed to reduce the impact of local dynamic changes and for an application to face recognition.…”

Section: Related Workmentioning

confidence: 99%

Reorganizing local image features with chaotic maps: an application to texture recognition

Florindo¹

2020

Preprint

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Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.

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Section: Related Workmentioning

confidence: 99%

Reorganizing local image features with chaotic maps: an application to texture recognition

Florindo¹

2020

Preprint

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“…Applications of chaos theory to image description are proposed, for example, in (Yu, 2017), where the authors develop an analytical model for the image function employing a Bezier parametric representation of the image surface and analyze complex chaotic patterns arising after successive iterations of such image function. They also provide a theoretical framework where they postulate a relation between the role that chaotic phenomena play in the brain and the interpretation of digital images.…”

Section: Related Workmentioning

confidence: 99%

“…Since its popularization in the sixties, chaos theory, in its modern sense, has attracted attention in numerous areas. Models for image processing and analysis have also been proposed, benefiting from tools originally developed for the analysis of chaotic systems (Gao and Yang, 2014;Rosin, 2010;Guo et al, 2010a;da Silva et al, 2015;Yu, 2017).…”

Section: Introductionmentioning

confidence: 99%

A cellular automata approach to local patterns for texture recognition

Florindo¹,

Metze²

2020

Preprint

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Texture recognition is one of the most important tasks in computer vision and, despite the recent success of learning-based approaches, there is still need for model-based solutions. This is especially the case when the amount of data available for training is not sufficiently large, a common situation in several applied areas, or when computational resources are limited. In this context, here we propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis. The method formulates a new transition function for the automaton inspired on local binary descriptors. It counterbalances the new state of each cell with the previous state, in this way introducing an idea of "controlled deterministic chaos". The descriptors are obtained from the distribution of cell states. The proposed descriptors are applied to the classification of texture images both on benchmark data sets and a real-world problem, i.e., that of identifying plant species based on the texture of their leaf surfaces. Our proposal outperforms other classical and state-of-the-art approaches, especially in the real-world problem, thus revealing its potential to be applied in numerous practical tasks involving texture recognition at some stage.

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“…Chaos theory is the study of apparently unpredictable or random behavior in systems governed by deterministic laws. 1 Due to the ergodicity, unpredictability, and dynamic properties; chaos has been efficiently used in many fields such as natural phenomena modeling, 2 secure communication, 3 image processing, 4 nonlinear circuits, 5 encryption, 6 and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…A new encoding and representation type, which is based on chaos, which finds the suitable ranges of attributes simultaneously and automatically along with the association rule mining process is proposed. 4. By the chaos numbers integrated into the proposed optimization method, modifying or changing of the quantitative valued data by discretizing and partitioning as preprocess is eliminated.…”

Section: Introductionmentioning

confidence: 99%

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos-enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos-based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems.The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods.According to the obtained results, the proposed method seems promising with respect to different metrics.

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Application of Chaos in Image Processing and Recognition

Cited by 8 publications

References 13 publications

Reorganizing local image features with chaotic maps: an application to texture recognition

Reorganizing local image features with chaotic maps: an application to texture recognition

A cellular automata approach to local patterns for texture recognition

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

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