# An Empirical Study on Ensemble of Segmentation Approaches
## 1 Introduction
This is the case for an instance of skin segmentation or butterfly identification. A drawback of this technology is based on the fact that a huge amount of data is needed to train these systems, but labeled data are a scarce resource in many domains. This is one of the reasons why big efforts are spent building and publishing datasets in specific areas; an example is Kvasir-SEG [8], a recent dataset that contains polyp images annotated at the pixel level by a group of experts. A novel architecture came from the scope of natural language processing (NLP), where researchers study how to comprehend the semantic of texts with the purpose of automating tasks such as summarization or translation. This new model called the transformer is designed with a self-attention mechanism that enables the system to focus on a specific part of the input. Transformers have also been applied to computer vision tasks, gaining performances comparable or even better than CNNs. Once again, the main drawback of these models resides in the high demand of data useful to train a stable and performing system. TransFuse [9] and UACANet [10] are two recent approaches in the medical domain that combine different techniques: the first is a combination of CNN kernels and transformers; while the second blends U-Net and a parallel axial attention autoencoder. No matter the architecture, the aim is to capture information at both local and global levels. As previously noticed, semantic segmentation becomes of main importance in many contexts. Autonomous vehicles, for instance, use semantic segmentation to identify objects in the environment surrounding the vehicle in order to make safe decisions [11]. In clinical practice, it helps in reducing the exposure to serious risks by detecting pathologies in their early stages, such as polyps detection that may prevent the evolution of colorectal cancers [2]. Similarly, in skin detection, deep learning methods are employed in various areas, spanning from face detection to hand gesture recognition [12]. In this context, deep approaches have faced some difficulties, such as the clutter in the background that hinders the reliable detection of hand gestures in real-world environments. CNNs have shown their appeal also to this context; two examples are the works from [PERSON] et al. [13] and [PERSON] et al. [14]. In the former work, the authors suggest using a CNN based on skin detection techniques to enhance the hand detector output. The latter instead introduces a residual skip connections (OR-Skip-Net) CNN that decreases the computational effort of the network and at the same time tackles demanding skin segmentation tasks; the goal is achieved by moving data to the last layer of the network directly from the initial layer. CNNs are also employed for automatically translating sign language [15]. A comparative analysis is reported in [12] through an extensive empirical evaluation of several leading technologies on a set of skin detection benchmarks. Recently, deep learning was also used for the automatic recognition and classification of leukocytes [16]. This practice helps medical practitioners to diagnose various blood-related diseases. This can be done in many different ways: practitioners can analyze the percentages of leukocytes with a histogram-based technique or with iterative algorithms (such as GrabCut [17]) that segment white blood cells. Contribution: this paper proposes a new ensemble method based on DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers' backbones. The proposal is intended to deal with semantic segmentation. In this work, diversity among individual classifiers in the ensemble is enforced by adopting different loss functions and testing different data augmentation approaches. We tested the proposed method on five different scenarios and compared the results with existing frameworks. The empirical evaluation highlights our results that are close to or even better than the state-of-the-art level.