In this paper we present the COLSHADE algorithm for real parameter constrained optimization problems. COLSHADE evolved from the basic L-SHADE algorithm by introducing significant features such as adaptive Lévy flights and dynamic tolerance (included in the constraint handling technique). Lévy flights mainly perform the exploration phase in algorithms such as the Firefly algorithm and Cuckoo search; in COLSHADE, however, the goal of the Lévy flights is to administer the selection pressure exerted over the population as to find the feasible region and keeping diversity. Thus, a new adaptive Lévy flight mutation operator is introduced here and called the levy/1/bin. In many problems the levy/1/bin excels during the exploration phase whilst the exploitation phase is performed by current-to-pbest mutation. However, the adaptive strategy propitiates the emergence of these two mutation approaches at different rates and times. The proposed method is tested on 57 constrained optimization functions of the benchmark provided for the CEC 2020 realworld single-objective constrained optimization competition.

Blood vessel extraction in digital retinal images is an important step in medical image analysis for abnormality detection and also obtaining good retinopathy diabetic diagnosis; this is often referred to as the Retinal Blood Vessel Segmentation task and current state-of-the-art approaches all use some form of neural networks. Designing neural network architecture and selecting appropriate hyper-parameters for a specific task is challenging. In recent works, increasingly more complex models are starting to appear, but in this work, we present a simple and small model with a very low number of parameters with good performance compared with the state of the art algorithms. In particular, we choose a standard Genetic Algorithm (GA) for selecting the parameters of the model and we use an expert-designed U-net based model, which has become a very popular tool in image segmentation problems. Experimental results show that GA is able to find a much shorter architecture and acceptable accuracy compared to the U-net manually designed. This finding puts on the right track to be able in the future to implement these models in portable applications

One of the issues that might affect the performance of Differential Evolution (DE) is premature convergence. In such cases, and especially in long-term executions, due to the nature of the reproduction phase of DE, computational resources might not be employed efficiently. In DE this issue is usually tackled by altering the reproduction phase or by attaching an external archive. However, in other fields such as in combinatorial optimization, methods that amend the replacement phase have been very successful. In this paper, two variants of DE are extended by including a replacement phase that explicitly relates the amount of diversity maintained in the population with the number of generations evolved and with the stopping criterion. One of the variants is the classic DE/rand/1/bin whereas the other one is Success-History based Adaptive DE (SHADE), a state-of-the-art approach. In the case of the classic variant, the principles employed in combinatorial optimization could be used straightforwardly. However, when integrating it with state-of-the-art techniques that already incorporate mechanisms to preserve diversity, additional modifications were required. Experimental validation has been performed with the benchmarks provided for the 2013 IEEE Congress on Evolutionary Computation competition on real parameter optimization. The results of the best-ranked DE in such a competition could be improved further. Additionally, some problems that had not been solved to optimality ever by any proposal, could be successfully solved.

We present a novel framework for image segmentation based on the maximum likelihood estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different likelihood functions called models. In this work, we construct on last hypothesis and, additionally, we assume that such samples come from independent and identically distributed random variables. Thus, the probability (likelihood) that a particular model generates the observed value (at a given pixel) is estimated by computing the likelihood of the sample composed with the surrounding pixels. This simple approach allows us to propose efficient segmentation methods able to deal with textured images. Our approach is naturally extended for combining different features. Experiments in interactive image segmentation, automatic stereo analysis and denoising of brain water diffusion multi-tensor fields demonstrate the capabilities of our approach.

We present a framework for image segmentation based on quadratic programming, i.e., by minimization of a quadratic regularized energy linearly constrained. In particular, we present a new variational derivation of the quadratic Markov measure field (QMMF) models, which can be understood as a procedure for regularizing model preferences (memberships or likelihoods). We also present efficient optimization algorithms. In the QMMFs, the uncertainty in the computed regularized probability measure field is controlled by penalizing Gini's coefficient, and hence, it affects the convexity of the quadratic programming problem. The convex case is reduced to the solution of a positive definite linear system, and for that case, an efficient Gauss-Seidel (GS) scheme is presented. On the other hand, we present an efficient projected GS with subspace minimization for optimizing the nonconvex case. We demonstrate the proposal capabilities by experiments and numerical comparisons with interactive two-class segmentation, as well as the simultaneous estimation of segmentation and (parametric and nonparametric) generative models. We present extensions to the original formulation for including color and texture clues, as well as imprecise user scribbles in an interactive framework.

Abstract We propose a general image and video editing method based on a Bayesian segmentation framework. In the first stage, classes are established from scribbles made by a user on the image. These scribbles can be considered as a multi‐map (multi‐label map) that defines the boundary conditions of a probability measure field to be computed for each pixel. In the second stage, the global minima of a positive definite quadratic cost function with linear constraints, is calculated to find the probability measure field. The components of such a probability measure field express the degree of each pixel belonging to spatially smooth classes. Finally, the computed probabilities (memberships) are used for defining the weights of a linear combination of user provided colours or effects associated to each class. The proposed method allows the application of different operators, selected interactively by the user, over part or the whole image without needing to recompute the memberships. We present applications to colourization, recolourization, editing and photomontage tasks.

A quadratic programming formulation for multiclass image segmentation is investigated. It is proved that, in the convex case, the non-negativity constraint on the recent reported quadratic Markov measure field model can be neglected and the solution preserves the probability measure property. This allows one to design efficient optimization algorithms. Additionally, it is proposed a (free parameter) inter-pixel affinity measure which is more related with classes memberships than with color or gray gradient based standard methods. Moreover, it is introduced a formulation for computing the pixel likelihoods by taking into account local context and texture properties.

We propose a gray scale image colorization method based on a Bayesian segmentation framework in which the classes are established from scribbles made by a user on the image. These scribbles can be considered as a multimap (multilabels map) that defines the boundary conditions of a probability measure field to be computed in each pixel. The components of such a probability measure field express the degree of belonging of each pixel to spatially smooth classes. In a first step we obtain the probability measure field by computing the global minima of a positive definite quadratic cost function with linear constraints. Then color is introduced in a second step through a pixelwise operation. The computed probabilities (memberships) are used for defining the weights of a simple linear combination of user provided colors associated to each class. An advantage of our method is that it allows us to re-colorize part or the whole image in an easy way, without need of recomputing the memberships (or /sp alpha/-channels).