## 3 Materials and Methods
### Inference Core of Luminance-Based Classification  
For luminance-based classification as mentioned in section 'Experimental Setup', HSL color model is used which allows the lightness calculation of the visual data that is in form of RGB values. For the calculation of perceived lightness of the scene, the values of visible light spectrum falling in electromagnetic spectrum are manipulated and observed for the illumination identification. As the light spectrum comprises of different wavelengths of colors including RGB residing in the visible light spectrum as seen in Figure 2, the same color intensity values of the scene as projected by the camera sensor. These color intensities are converted and calculated for further lightness calculation of the captured scene. The HSL color model identifies pixel luminosity based on Equation (1), we further divide this formula with 255 to keep the luminance value of the pixel in range of 0-1; 0 being the darkest and 1 being the brightest value of the pixel. \[RL=RL=\frac{0.2125\times R+0.715\times G+0.072\times B}{255} \tag{1}\]  
_RL_ represents the relative luminance of the image pixel being processed. The weights associated to each color channel in Equation (1), are derived from the HSL color model's details for the calculation of single pixel luminosity. The luminosity calculation is backed by the Photometric/digital ITU BT.709 standardized parameters for conversion of RGB values into respective luminance. The equation refers to luminosity function representing the perceived lightness of the visual scene. The contributors of the lightness have following order green, red and blue, i.e., green component contributes the most whereas blue component contributes the least. Generally, all the associated chromaticity is positive co-efficient which result in RGB to Luminance conversion transformational base. Based on the identified pixel values we performed extensive analysis over the annotated image dataset to finally propose and present the identified spectrums for Dark, Semi-dark and Bright in terms of luminosity of the image. Figure 4 presents the idea of the electromagnetic spectrum and its part of visible light on top of the visible light spectrum we present novel spectrum identifying the ranges and thereby, these ranges can be used to eliminate visual extremes such as dark and bright. Figure 4: Relative luminance spectrum in visible light. Based on the identified spectrums we further propose a well defined framework for the classification of existing image data into different categories as suggested by the thresholds. The novel algorithm working at the backend of framework is discussed in Proposed Algorithm 1. ```
0: An image I of N pixels and spectrum thresholds \(\theta_{DS}\) and \(\theta_{SS}\). 0: luminance-based label L on image \(I\). 1: Pass I with dimensions \(H\) \(w\) \(\times\) C \(\rightarrow\) Number of color channel C \(\forall\) I \(\rightarrow\) 3
2: Split \(I\) into three multidimensional arrays \(H\) \(\times\) \(W\) \(\times\) \(G\) \(H\) \(\times\) \(W\) \(\times\) \(B\) \(H\) \(\times\) \(W\) \(B\)
```  
**Algorithm 1** Relative Perceived Luminance Classification  
The algorithm starts with prior information of existing image and the proposed spectrum details as thresholds \(\theta_{DS}\) and \(\theta_{SB}\), where \(\theta_{DS}\) is threshold between Dark and Semi-dark spectrum and \(\theta_{SB}\) is threshold between Semi-dark and Bright spectrum. For every image pixel having dimensions \(H\) \(\times\)\(W\) \(\times\) 3, 3 being the color channel information every image is iteratively processed for pixel luminosity identification. Here the dimensions of the image explicitly represented by 'H' being Height, 'W' being Width and '3' represent the associated color channel (RGB) information for further pixel manipulations. For the luminosity calculation, Relative Luminance (RL) calculation formula presented in Equation (1) is employed.