Abstract
We propose an explainable model for classifying the color of pixels in images. We propose a method based on binary search trees and a large peer-labeled color name dataset, allowing us to synthesize the average human perception of colors. We test our method on the application of Person Search. In this context, persons are described from their semantic parts, such as hat, shirt, ... and person search consists in looking for people based on these descriptions. We label segments of pedestrians with their associated colors and evaluate our solution on datasets such as PCN and Colorful-Fashion. We show a precision as high as 83% as well as the model ability to generalize to multiple domains with no retraining.
Keywords
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The data is available at https://github.com/Smoltbob/XKCDColors-Dataset.
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Acknowlegments
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [CRDPJ 528786 - 18], and the support of Arcturus network.
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Simon, J., Bilodeau, GA., Steele, D., Mahadik, H. (2020). Color Inference from Semantic Labeling for Person Search in Videos. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_13
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