## 2 Methodology
### Image Parsing  
The original image processing techniques utilized in analytical re-ID were applied to full images, primarily due to the lack of reliable segmentation techniques [21]. This resulted in feature vectors of different subregions becoming mixed, thus complicating the similarity estimation. The implementation of preliminary image parsing--as well as, specifically, human parsing--is a promising technique that allows one to divide an object into subregions that correspond to specific classes. Whereas clustering-based segmentation is well-known, it is also computationally expensive and does not provide sufficient accuracy. Spatiotemporal features, which were based on a graph partitioning of the full image, were proposed to localize feature vectors to specific regions [22]. More recently, a simple approach was proposed that leveraged the fact that body parts in different images ordinarily appeared in the same image sections in re-ID datasets. This allowed one to separate images into three or more subregions, as well as to calculate local feature vectors for horizontal strips [26; 27; 28]. In this way, the information about the head, torso, and legs could be obtained. Whereas otherwise being computationally convenient, this method was not robust in scenarios that included pose change and partial occlusion. Furthermore, the information regarding the difference between elements within one strip, such as hands and torso, was lost. This was addressed via several modifications that were based on a more detailed representation of the human body structure [5; 29]. Nevertheless, the issues of background subtraction, the exact region boundary estimation, as well as the body part or clothes classification remained a challenge--which human parsing allowed one to address. The main goal of human parsing is to provide a mask that assigns a specific class to every image pixel. For re-ID purposes, these include body parts, hair, clothes, and other wearables. Whereas this task can also be performed by more sophisticated general purpose image segmentation tools, they are unnecessarily complex due to redundant classes, such as furniture, animals, or vehicles--which are all irrelevant for the purposes of person segmentation [30]. Human parsing is usually performed by neural networks that are trained on human parsing datasets, e.g., LIP or Pascal [31; 32]. Several studies have shown that  
Figure 1: Method overview: parsing of the query image for the per-class feature extraction with consequent comparison with dataset images, which is conducted in order to form a similarity ranking. incorporating human parsing into a re-ID framework significantly improves the prediction accuracy [25; 33]. However, existing solutions still utilize additional neural networks for feature extraction, metric learning, feature importance estimation (or attention), and decision making, thereby resulting in complex architectures [25; 33; 34; 35]. In this paper, we propose to combine recent advances in human parsing with analytical feature extraction. Human parsing naturally handles aspects that are challenging for analytical methods by providing the initial segmentation of an object into subregions. As every subregion is assigned its class, this allows a comparison of feature vectors of the same class without dealing with the noise originating from neighboring subregions. The shape of the subregions mimics the real shape of the elements in the image, which pairs well with methods that do not require one to apply filters to subregions for the purposes of feature extraction. In this work, we use an out-of-the-box human parser SCHP [36; 37]. This parser was trained on LIP dataset for human parsing by authors of the original study. It should be stressed that the parser was used \"as is\" and that no additional training was performed in this study. For any pair of images that were parsed, feature vectors were compared only for classes present in both images.