# An Empirical Study on Ensemble of Segmentation Approaches
## 1 Introduction  
Being able to recognize objects in images has been, for a long time, a prerogative of human beings. It has taken over 14 years to reach the level of an untrained human in the challenge of Imagenet. Things become more complex when the task requires not only to recognize the object in an image but also to identify its boundaries. This task is called semantic segmentation, and in machine learning this entails the classification of each pixel in an image. Due to the improvements of performance related to the adoption of machine learning models, this task is applied to many real-life scenarios [1, 2]: in clinical practice, it can be used to identify polyps; similarly, in skin and blood analysis the identification of objects may help to visually bind the presence of different types of diseases. In addition, it can be used in autonomous vehicles, to identify objects surrounding the vehicle, in environmental microorganisms' classification, and many others. The standard approach is to train a system composed of two modules: an encoder, and a decoder. The first module learns a low-dimensional representation of the input that describes semantics in the image. The second module learns to build the original input based on this low-dimensional feature vector. This has been the approach adopted by U-Net [3], one of the first systems developed for semantic segmentation. Autoencoders [4] were also employed to resolve the task due to their ability to learn semantics' low-levelrepresentations of an image through the encoder module and the ability of reconstructing the original input from this reduced representation. Autoencoders' performance and results are the reasons why many researchers and practitioners from the computer vision area have adopted them. The performance of autoencoders, as well as the ones of other classifier technologies, are strongly affected by architecture configuration, and other configurations often referred to as hyper-parameters tuning. That consists in finding the best values of some attributes of the model. This is a context-specific task that requires domain knowledge as well as expertise with the adopted machine learning techniques, resulting in big efforts and time consumption. The well-known no-free lunch theorem for machine learning highlights that a single model that works well on all the datasets cannot exist. Based on this evidence, another approach consists of adopting sets of classifiers, often shallow or weak, whose predictions are aggregated to form the output of the system. These frameworks are called ensemble methods. In an ensemble, individual classifiers are trained on the same dataset, in such a way, each model should generalize differently in the training space. Ensembles provide state-of-the-art results in many domains, but it is important to ensure some properties. One of them is to enforce some kind of diversity in the set of classifiers. In this work, we propose a novel ensemble method for semantic segmentation. Our model is based on convolutional neural networks (CNNs) and transformers. Diversity among individual classifiers is enforced by adopting different loss functions and testing different data augmentations. The model has been developed by combining DeepLabV3+ [5], HarDNet-MSEG [6], and Pyramid Vision Transformers [7]. The developed solution is then assessed through an extensive empirical evaluation that compares our proposal with state-of-the-art solutions highlighting promising results often better than best approaches. The evaluation has been carried out on five different real-world scenarios, namely polyp segmentation, skin segmentation, butterfly recognition, environmental microorganisms' classification, and leukocyte detection. Due to improvements of the discipline, machine learning techniques are used and applied in many different areas, for instance in medical diagnosis or in biology. Convolutional neural networks (CNNs) and other classic predictors are adopted for assisting researchers and practitioners in better identifying objects in images.