Composing photomosaic images using clustering based evolutionary programming

Abstract

Photomosaic images are a type of images consisting of various tiny images. A complete form can be seen clearly by viewing it from a long distance. Small tiny images which replace blocks of the original image can be seen clearly by viewing it from a short distance. In the past, many algorithms have been proposed trying to automatically compose photomosaic images. Most of these algorithms are designed with greedy algorithms to match the blocks with the tiny images. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. With the constraint, the photomosaic problem becomes a combinatorial optimization problem. Evolutionary algorithms imitating the process of natural selection are popular and powerful in combinatorial optimization problems. However, little work has been done on applying evolutionary algorithms to photomosaic problem. In this paper, we present an algorithm called clustering based evolutionary programming to deal with the problem. We give prior knowledge to the optimization algorithm which makes the optimization process converges faster. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. The results indicate that our algorithm performs the best.

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

“…Metaheuristics generally have three main classes, including evolutionary algorithms (EA), physics-based algorithms (PA), and swarm intelligence (SI). EA is inspired by the evolution in nature, and some typical algorithms in this branch are GA [20], differential evolution (DE) [21], evolutionary programing (EP) [22], genetic programing (GP) [23], and biogeography-based optimizer (BBO) [24]. PA is derived from imitating physical rules, and some of the most popular algorithms are simulated annealing (SA) [25], gravitational local search (GLS) [26], bigbang-bigcrunch (BBBC) [27], charged system search (CSS) [28], and central force optimization (CFO) [29].…”