What is the difference between HLC (Histogram of local features) , CSS ( color self-similarity) ans MDST (Max DisSimilarity of Different Templates)

I'm new to computer vision and have been researching for Master thesis purposes in Detection algorithms and the techniques used in each. As I arrived to the point where alot of papers showed the importance of color in object recognition, i got got bumped with HLC MDST and CSS. So my question is : are they all literlally a way to describe the distribution of the color in an image? If yes I would be glad for a brief explanation ,what does each of them do, and a dedicated source link for that is available would be great. The three methods are briefly viewed in Qingyuan Wang1's work.Justifying the importance of color cues in object detection: a case study on pedestrian

Thanks in advance

Topic object-detection historgram image-recognition computer-vision machine-learning

Category Data Science

About

Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.