## 2 Methodology
### Texture Similarity  
Analytical texture descriptors are often separated into two broad categories: shape abstraction and local texture descriptors. Prominent examples of the former are skeletal [43] and morphological [44] filters that deal with the overall geometrical structure, the object's shape, and the subregion boundaries. However, human parsing allows us to segment images and classify subregions, so the shape descriptors provide insufficient information. This is because the two objects that are already classified as \"upper clothes\" or \"pants\" have a very similar structure. Furthermore, shape descriptors are not necessarily affine-invariant, which is a disadvantage for re-ID applications. In this work, we focus on the local texture descriptors that extract subregion-specific information about the object's texture. This is achieved using filters or operations on the pixels and their neighborhoods [45]. In prior work, filter packs that included Gabor filters were found to perform well on re-ID tasks [21]. However, such filters provided desirable translation and affine-invariant texture descriptors. As such, the necessity to fine-tune filter parameters led to a significant number of filters that were required to analyze a sufficiently vast variety of different textures. To avoid this, we have chosen the local binary pattern (LBP) method, due to its ability to generate texture descriptors. LBP is a local texture descriptor that was found to be extremely useful in texture and face classification. It assigns every pixel of a grayscale image a binary number with bits representing whether the pixel neighbor's intensity is greater than that of a central pixel. In the simplest case of only checking the closest neighbors (radius \(r=1\)), we obtain an 8-bit binary number and convert it to decimal. All decimal numbers of the image are combined into a 256-bin histogram. It is important to note that every bin corresponds to a unique texture type, which can be significantly different for neighboring bins. Over the years, various modifications to the original LBP have been proposed, including multiscale extensions [46], spatial enhancements [47], and texture uniformity classifications [48]. These methods often include a concatenation of several local histograms in order to encode additional spatial information. However, such modifications are not suitable for re-ID purposes, due to the fact that they are not translation and rotation invariant. This is acceptable for face recognition tasks, where distinct facial features are always localized in the same regions of an image. However, this is not suitable for re-ID images, where the point of view and object orientation can change dramatically. The separation of textures into uniform and non-uniform classes, which was found to be useful in many face recognition tasks, did not perform well here. The reason lies in the significant reduction in the number of non-zero bins and the loss of some descriptive information. Whereas for the face recognition tasks, uniform patterns were found to be the most informative. However, this appears to not be the case for re-ID tasks. Therefore, we use the original LBP method with one slight modification. It was shown that LBP can be made rotation and affine transformation invariant by performing a circular bit-wise right shift on the binary numbers [49]. Whereas this also reduces the number of possible non-zero bins, these invariances are essential for re-ID. As such, this modification was found to improve the performance in our experiments. In this work, we calculate two LBP histograms for every class of interest in the image. They correspond to the LBP descriptions of the subregion's contour and its inner area. It was found that separating them positively affects the similarity estimation, due to the fact that contour similarity varies in a wider range. In addition, its variations are not trivial with respect to changes in the inner area's similarity.