Multilevel Image Research Articles

Multilevel Image Thresholding Using Tsallis Entropy and Cooperative Pigeon-inspired Optimization Bionic Algorithm

Multilevel thresholding is a simple and effective method in numerous image segmentation applications. In this paper, we propose a new multilevel thresholding method that uses cooperative pigeon-inspired optimization algorithm with dynamic distance threshold (CPIOD) for boosting applicability and the practicality of the optimum thresholding techniques. Firstly, we employ the cooperative behavior in the map and compass operator of the pigeon-inspired optimization algorithm to overcome the “curse of dimensionality” and help the algorithm converge fast. Then, a distance threshold is added to maintain the diversity of the pigeon population and increase the vitality to avoid local optimization. Tsallis entropy is used as the objective function to evaluate the optimum thresholds for the considered gray scale images. Four benchmark images are applied to test the property and the stability of the proposed CPIOD algorithm and three other optimization algorithms in multilevel thresholding problems. Segmentation results of four optimization algorithms show that CPIOD algorithm can not only get higher quality segmentation results, but also has better stability.

Image Segmentation Using Multilevel Thresholding: A Research Review

Image segmentation is a basic problem in computer vision and various image processing applications. Over the years, commonly used image segmentation has become quite challenging because of its utilization in many applications. Image thresholding is one of the most exploited techniques to accomplish image segmentation. Multilevel thresholding is found to be most appropriate and well known among all the image segmentation techniques. The segmented image quality is based on the techniques incorporated to choose the threshold value. In this paper, an exhaustive survey has been carried out, considering both the general purpose and satellite images to cover the performance comparison of various image segmentation approaches based on meta-heuristics optimization algorithms, present in the literature for multilevel image thresholding. In addition, this paper also focuses on information theoretic approach-based objective criterion using different statistical properties such as between-class variance, entropy, moment and maximum likelihood for selecting multilevel thresholds. A list of 157 publications on the subject is also appended for quick reference.

Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field.

Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy

This paper presents a multilevel image thresholding approach which relies on Tsallis entropy using Fuzzy partition with a novel threshold selection technique. In order to compute the optimal threshold values, Differential Evolution (DE) has been employed. The proposed method can further be exploited in image segmentation which is considered to be a critical step in image processing. Our proposed threshold selection technique is based on Tsallis-Fuzzy entropy and the results are compared with Shannon entropy (or fuzzy entropy) and Tsallis entropy based existing threshold selection techniques. The experiments are performed on two different sets of images and the results have been compared with that of existing state-of-the-art methods, namely, Patch Levy Bees' Algorithm (PLBA), Bacterial Foraging optimization (BFO), modified Bacterial Foraging optimization (MBFO) and Bees' Algorithm (BA). Quantitative analysis is carried out based on three image quality metrics viz SSIM, PSNR and SNR. Standard deviation and CPU time for convergence of the objective function have been calculated for performance evaluation. Furthermore, the statistical significance of our method has been estimated using Friedman test and Wilcoxon test. The experimental results manifest that our method produces results superior to the methods in comparison.

PSO image thresholding on images compressed via fuzzy transforms

We present a new multi-level image thresholding method in which a Chaotic Darwinian Particle Swarm Optimization algorithm is applied on images compressed by using Fuzzy Transforms. The method requires a partition of the pixels of the image under several thresholds which are obtained by maximizing a fuzzy entropy. The usage of compressed images produces benefits in terms of execution CPU times. In a pre-processing phase the best compression rate is found by comparing the grey level histograms of the source and compressed images. Comparisons with the classical Darwinian Particle Swarm Optimization multi-level image thresholding algorithm and other meta-heuristic algorithms are presented in terms of quality of the segmented image via PSNR and SSIM.

Multi-level image thresholding based on social spider algorithm for global optimization

Thresholding is one of the simplest and popular technique for segmenting images. Maximum between-class variance (Otsu’s) method is one of the well-known and widely used method in case of segmentation. Not only Otsu could be used for bi-level thresholding but also it could be extended to multi-level image thresholding. Finding the optimum threshold values in multi-level case is very time consuming process, thus optimization algorithm can deal with this problem. In this paper social spider algorithm for global optimization has been used for maximizing the between-class variance to carry out multi-level image thresholding. Experimental outcomes have demonstrated that the proposed method is capable of estimating threshold values and yield satisfying outcome.

Phase retrieval algorithm for optical information security**Project supported by the National Natural Science Foundation of China (Grant Nos. 61775121, 61605165, 61307003, 61405122, and 11574311), the Key Research and Development Program of Shandong Province, China (Grant No. 2018GGX101002), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2019QF006), and the Fundamental Research Funds of Shandong University, China (Grant No. 2015GN031).

As a typical technology for optical encryption, phase retrieval algorithms have been widely used in optical information encryption and authentication systems. This paper presents three applications of two-dimensional (2D) phase retrieval for optical encryption and authentication: first, a hierarchical image encryption system, by which multiple images can be hidden into cascaded multiple phase masks; second, a multilevel image authentication system, which combines (t, n) threshold secret sharing (both t and n are positive integers, and ) and phase retrieval, and provides both high-level and low-level authentication; and finally, a hierarchical multilevel authentication system that combines the secret sharing scheme based on basic vector operations and the phase retrieval, by which more certification images can be encoded into multiple cascaded phase masks of different hierarchical levels. These three phase retrieval algorithms can effectively illustrate phase-retrieval-based optical information security. The principles and features of each phase-retrieval-based optical security method are analyzed and discussed. It is hoped that this review will illustrate the current development of phase retrieval algorithms for optical information security and will also shed light on the future development of phase retrieval algorithms for optical information security.

An improved bat algorithm and its application in multi-level image segmentation

An improved bat algorithm and its application in multi-level image segmentation

Multi-level diversification approach of semantic-based image retrieval results

With the increasing popularity of social photograph-sharing Web sites, a huge mass of digital images, associated with a set of tags voluntarily introduced by amateur photographers, is daily hosted and consequently, the Tag-based social Image Retrieval technique has been widely adopted. However, tag-based queries are often too ambiguous and abstract to be considered as an efficient solution for the retrieval of the most relevant images that meet the users’ needs. As an alternative, the Semantic-based social Image Retrieval technique has emerged for the purpose of retrieving the relevant images covering as much possible the topics that a given ambiguous query (q) may have. Actually, the diversification strategies are a great challenge for researchers. In this context, we jointly investigate two processes at the ambiguous query preprocessing and postprocessing levels. On the one hand, we propose a Tag-based Query Semantic Reformulation process, which aims at reformulating the tag-based users’ queries, according to multiple semantic facets of the different images’ views, by using a set of predefined ontological semantic rules. On the other hand, we propose a Multi-level Image Diversification process that can first perform a two-level-based image clustering offline, and second, filter and re-rank the image cluster retrieval results according to their pertinence versus the reformulated query online. The experimental results and statistical analysis performed on a collection of 25.000 socio-tagged images shared on Flickr demonstrate the effectiveness of the proposed technique, which is compared with the research technique based on one-level-based image clustering, tag-based image research technique and recent CBIR techniques.

Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.

An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring

Efficient analysis of parking slot occupancy is still a complex task in reason of the variety of slot textures and of the difficulty to characterize the relevant information of their associated images. In this paper, we propose a handcrafted approach supported by machine learning techniques. The two main contributions are as follows: Firstly, we introduce a compact handcrafted image descriptor, named pyramid multi-level descriptor (PMLD), designed to capture features at different scales and at different receptive fields in the image region. Secondly, we provide a comparative study of several popular image-based handcrafted and deep learning features. Experiments are conducted on two public datasets: PKLot and CNRPark. It follows that PMLD achieves better results than classical handcrafted descriptors and achieves similar results to those obtained by transfer learning of the deep CNN VGG-F.

A multi-level residual reconstruction based image compressed sensing recovery scheme

Traditional image compressed sensing recovery focuses on the research of sparse representation and reweight processing of measurements. However, the image content and structural feature vary dramatically in different natural images. The improvements in reconstruction quality brought by exploring new sparse representation models are not satisfactory. This paper focuses on a multimedia application scenario where the encoder is resource-constrained and the decoder has powerful computing ability. A novel image compressed sensing recovery scheme based on multi-level residual reconstruction is proposed to further improve the reconstruction quality. By converting the original image recovery to the multi-level residual image recovery, the reconstruction process is divided into three phases. The hidden information in image recovery is fully utilized. Moreover, a constraint-adaptive recovery model is proposed to perform the initial reconstruction of the original image and the initial residual image reconstruction. Combining the multihypothesis prediction, the final recovered residual image is obtained in the secondary residual image recovery phase. The final recovered image is obtained by combining the recovered original image and the residual image. Experimental results show that our proposal outperforms the state-of-the-art methods for image compressed sensing reconstruction in both objective and subjective quality.

Oppositional elephant herding optimization with dynamic Cauchy mutation for multilevel image thresholding

This paper presents an improved elephant herding optimization (IEHO) to solve the multilevel image thresholding problem for image segmentation by introducing oppositional-based learning (OBL) and dynamic cauchy mutation (DCM). OBL accelerates the convergence rate and enhances the performance of standard EHO whereas DCM mitigates the premature convergence. The suggested optimization approach maximizes two popular objective functions: ‘Kapur’s entropy’ and ‘between-class variance’ to estimate optimized threshold values for segmentation of the image. The performance of the proposed technique is verified on a set of test images taken from the benchmark Berkeley segmentation dataset. The results are analyzed and compared with conventional EHO and other four popular recent metaheuristic algorithms namely cuckoo search, artificial bee colony, bat algorithm, particle swarm optimization and one classical method named dynamic programming found from the literature. Experimental results show that the proposed IEHO provides promising performance compared to other methods in view of optimized fitness value, peak signal-to-noise ratio, structure similarity index and feature similarity index. The suggested algorithm also has better convergence than the other methods taken into consideration.

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.

A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

In this paper, a novel context sensitive energy curve based Masi entropy for image segmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution for image segmentation but the performance is not satisfactory. However, better results can be obtained using the concept of energy curve for Masi entropy, but it consumes more time and also the complexity level for selecting suitable thresholds is high. MSA is a newly developed stochastic meta-heuristic optimization algorithm introduced after observing, mimicking and modeling the life cycle of moth swarm. It is used to simplify the problem of extensive exploration for finding the optimum threshold values and to increase the quality of the images. Experiments on standard daily-life color images are showed to establish the usefulness of the presented approach. The Energy-Masi-MSA technique is examined intensively regarding visual quality and quantitative matrices are considered to evaluate the results of the Energy-Masi-MSA scheme compared to existing methods. Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost. Experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.

A multi level image steganography methodology based on adaptive PMS and block based pixel swapping

The practice of information exchange through global media like internet extensively entails a security concern. This paper consists of a proposed approach that braces the security concern in the realm of image steganography. The adaptive technique of the Power Modulus Scrambling (PMS) has been preliminarily used so as to disturb the normal pixel orientation of the original carrier. A second layer of encryption is enforced with the implementation of the Block Based Pixel Swapping technique. These steps ensure a two tier secured shield. Next, the embedding procedure is facilitated depending on a comparative key based permutation combination methodology. This approach caters the need of security during communication. The proposed approach is evaluated with respect to substantial performance metrics. The commendable results obtained shows that the foothold of imperceptibility is well maintained.

The image of a corrupt official as perceived by Russia’s youth: using the unfinished sentences method

This article considers the image of a corrupt official as perceived by young Russians. The object of this study was represented by students from three Moscow-based universities. The survey was carried out in 2018. The sample consisted of 118 people ages 17 to 21. The results of the analysis show that, in order to develop anti-corruption measures in the field of education, it is necessary to study those personal, professional and behavioral image characteristics of a corrupt official which are relevant to young people. With this goal in mind, a sociological study was conducted in 2018 utilizing the unfinished sentences method, with students from three Russian universities participating in the study. The results of the study reflect a multilevel collective image, defined by the behavioral characteristics of a corrupt official. A conclusion was drawn that, while describing individual traits of a corrupt official, personal qualities, field of professional activities and means of interacting with others bear special significance. In recreating an image, the mentality and willpower of corrupt officials are shown in a light which points to mixed attitudes towards the person initiating acts of corruption. Meanwhile certain areas are highlighted where said image becomes somewhat blurred, with corrupt officials being regarded as “regular” people without any distinctive traits. It is suggested that these thoroughly analyzed substantive qualities of the image of a corrupt official should be considered while developing measures for cultivating intolerant attitudes among young people towards the main subjects of acts of corruption.

An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding

Multilevel image thresholding is an important technique for image processing. However, the computational complexity of multilevel image thresholding grows exponentially with the increase in the number of thresholds when using the exhaustive searching method. To address this problem, a plenty of heuristic algorithms are applied to search the optimal thresholds. In this paper, an improved flower pollination algorithm (IFPA) using Tsallis entropy as its objective function is presented to find the optimal multilevel thresholding. In the IFPA, three modifications are utilized to enhance the flower pollination algorithm (FPA). First, an adaptive switch probability method is used to balance the local and global pollination. Second, a new local pollination strategy is adopted to avoid the population falling into local optimum. Third, an crossover and selection operations are applied to the FPA which can increase the diversity of the population, then enhancing the performance of the FPA. Subsequently, three different algorithms such as FPA, GSA and DE are introduced to compare with the IFPA in the experiments. The experimental results demonstrated that the IFPA can search out the optimal thresholds effectively, accurately and can obtain the best image segmentation quality.

A Multilevel Image Thresholding Based on Hybrid Salp Swarm Algorithm and Fuzzy Entropy

The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value.

A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation

Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.