7 research outputs found
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets
Decision trees are essential yet NP-complete to train, prompting the
widespread use of heuristic methods such as CART, which suffers from
sub-optimal performance due to its greedy nature. Recently, breakthroughs in
finding optimal decision trees have emerged; however, these methods still face
significant computational costs and struggle with continuous features in
large-scale datasets and deep trees. To address these limitations, we introduce
a moving-horizon differential evolution algorithm for classification trees with
continuous features (MH-DEOCT). Our approach consists of a discrete tree
decoding method that eliminates duplicated searches between adjacent samples, a
GPU-accelerated implementation that significantly reduces running time, and a
moving-horizon strategy that iteratively trains shallow subtrees at each node
to balance the vision and optimizer capability. Comprehensive studies on 68 UCI
datasets demonstrate that our approach outperforms the heuristic method CART on
training and testing accuracy by an average of 3.44% and 1.71%, respectively.
Moreover, these numerical studies empirically demonstrate that MH-DEOCT
achieves near-optimal performance (only 0.38% and 0.06% worse than the global
optimal method on training and testing, respectively), while it offers
remarkable scalability for deep trees (e.g., depth=8) and large-scale datasets
(e.g., ten million samples).Comment: 36 pages (13 pages for the main body, 23 pages for the appendix), 7
figure
A Multiobjective Approach to Homography Estimation
In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm
Bioinspired metaheuristics for image segmentation
Advisors: Erik Cuevas, Humberto Sossa. Date and location of the PhD thesis defense: 2nd December 2013, Centro de Investigación en Computación - Instituto Politécnico Nacional.In general, the purpose of Global Optimization (GO) is finding the global optimum of an objective function defined inside a search space. The GO has applications in many areas of science, engineering, economics, among other, where mathematical models are utilized. Those algorithms are divided into two groups: deterministic, and evolutionary. Since deterministic methods only provide a theoretical guarantee of locating local minimums of the objective function, they face great difficulties in solving GO problems. On the other hand, evolutionary methods are faster in locating a global optimum than deterministic ones, because they operate over a population of candidate solutions, therefore they have a bigger likelihood of finding the global optimum, and a better adaptation to black box formulations or complicated function forms
A Stigmergy-Based Differential Evolution
Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems
A Stigmergy-Based Differential Evolution
Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems