Battle Royale Optimizer for solving binary optimization problems

Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, which utilizes a dissimilarity measure between binary vectors instead of a vector subtraction operator, used in the original BRO algorithm to find the nearest neighbor. To evaluate BinBRO, we applied it to two popular benchmark datasets: the uncapacitated facility location problem (UFLP) and the maximum-cut (Max-Cut) graph problems from OR-Library. An open-source MATLAB implementation of BinBRO is available on CodeOcean and GitHub websites.Publisher's Versio

Improving bag-of-poses with semi-temporal pose descriptors for skeleton-based action recognition

International audienceOver the last few decades, human action recognition has become one of the most challenging tasks in the field of computer vision. Employing economical depth sensors such as Microsoft Kinect as well as recent successes of deep learning approaches in image understanding has led to effortless and accurate extraction of 3D skeleton information. In this study, we have introduced a novel bag-of-poses framework for action recognition by exploiting 3D skeleton data. Our assumption is that any action can be represented with a set of predefined spatiotemporal poses. The pose de-scriptor is composed of two parts, the first part is concatena-tion of the normalized coordinate of the skeleton joints. The second part consists of temporal displacement of the joints which is constructed with predefined temporal offset. In order to generate the key poses, we apply K-means clustering overall training pose descriptors of dataset. To classify an action pose, we train a SVM classifier with the generated key poses. Thereby, every action on dataset is encoded with key-poses histogram. We use ELM classifier to recognize the actions since it has been shown to be faster, accurate , and more reliable than other classifiers. The proposed framework is validated with four publicly available benchmark 3D action datasets. The results show that our frame-2 Saeid Agahian et al. work achieves state-of-the-art results on three of the datasets compared to the other methods and produces competitive result on the fourth

BinBRO: Binary Battle Royale Optimizer algorithm

Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.Publisher's VersionWOS:00078728100001