## 1 Introduction
### Gap Analysis & Contribution  
The diversity of the visual scene has high impact on the overall performance of categorization of the scene in terms of the visibility of the image [6]. The studies reviewed relating to the automated solutions and visual complexities are limited to controlled visual environments [17] and restricted hardware usage, which means it is difficult to generalize any pattern for the extreme visual interpretative frameworks. Regardless of all the advancements made in the domain of automated image classification, the current knowledgebase in this domain is limited in following aspects:  
* Absence of literary and experimental studies relating to the semi-dark visual scenarios. * Unknown correlating image feature for image classification/categorization of semi-dark images in discourse of ML techniques. * No well-defined CV classification module for classification of semi-dark images. * Limited information of luminance based classifying thresholds in CV techniques. To bridge this gap and classify the huge amount of existing image data representing problem domain data, we present an exclusive study with following aspects:Literary analysis of the state-of-the-art image classifiers with respect to luminance and their shortcomings  
A critical comparative analysis of existing image classifiers mentioning the problem domain i.e., the illumination of a visual scene. We also discuss the details of these classifiers and their integration with the relevant image feature for image classification. To the best of our knowledge, at the time of writing this manuscript, no study has highlighted this issue and presented such a comparative analysis. This analysis is critical for opening future research directions for better classification of semi-dark images. 2 Proposal of novel image classification method subjected to correlating image features  
An image classification algorithm is presented subjected to the correlating feature, i.e., relative luminance. A luminance-based classification is proposed along with the luminance spectrums backed by manipulation of HSL (hue, saturation, lightness) color model. These spectrums highlight moderate visual scenarios (semi-dark spectrum) for further/subsequent optimal image processing. This helps in elimination of the visual extremes (i.e., dark, and bright) to represent semi-dark images for subsequent processing. 3 Validation of novel algorithm named Relative Perceived Luminance Classification (RPLC) for image classification based on image luminosity  
The proposed luminance-based classification algorithm is extensively validated over variant sized images based on the luminance spectrums. We have also comprehensively presented the design and implementation of novel deterministic protocol based on color model for luminance identification of image to eliminate visual extremes. The presented research formulizes highly tunable algorithm based on simple statistical inference along with the luminosity spectrum values for classification of the images in dark, semi-dark and bright image labels. The benchmarking of the proposed algorithm is performed against CNN and SVM based image classifiers. 4 Enhancement and labelling of existing datasets using RPLC  
For the validation of the proposed algorithm, we extracted images from known datasets of MS COCO [18] and ExDark [19]. After a thorough investigation of visual images, the luminosity values were identified. These findings were further used for annotation of existing visual data. Hence, using the proposed algorithm, we have enhanced and labelled image datasets of MS COCO, Berkeley, and Stanford as dark, semi-dark and bright. We have open sourced the improvised datasets for research community. To the best of our knowledge, there is not any well-defined method presented so far for the identification/classification of existing images based on the luminance values of the captured visual image.