Papers › Color inference from semantic labeling for person search in videos
Color inference from semantic labeling for person search in videos
Jules Simon, Guillaume-Alexandre Bilodeau, David Steele, Harshad Mahadik
We propose an explainable model to generate semantic color labels for person search. In this context, persons are described from their semantic parts, such as hat, shirt, etc. Person search consists in looking for people based on these descriptions. In this work, we aim to improve the accuracy of color labels for people. Our goal is to handle the high variability of human perception. Existing solutions are based on hand-crafted features or learnt features that are not explainable. Moreover most of them only focus on a limited set of colors. We propose a method based on binary search trees and a large peer-labelled color name dataset. This allows us to synthesize the human perception of colors. Using semantic segmentation and our color labeling method, we label segments of pedestrians with their associated colors. We evaluate our solution on person search on datasets such as PCN, and show a precision as high as 80.4%.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections