Abstract
In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces the number of parameters needed for illumination estimation. At the same time, the proposed method is consistent with the color constancy assumption stating that global spatial information is not relevant for illumination estimation and local information ( edges, etc.) is sufficient. Furthermore, BoCF is consistent with color constancy statistical approaches and can be interpreted as a learning-based generalization of many statistical approaches. To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention. BoCF approach and its variants achieve competitive, compared to the state of the art, results while requiring much fewer parameters on three benchmark datasets: ColorChecker RECommended, INTEL-TUT version 2, and NUS8.
Highlights
In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling
In order to reduce the number of parameters needed to learn the illumination [6], [7], we propose a novel color constancy approach based on the Bag-of-Features Pooling [17], called the BoCF approach
In Subsection V-A, different topologies for the three blocks of BoCF are evaluated on the ColorChecker RECommended dataset and the effect of each block in our model is examined by reporting the results of the ablation studies
Summary
We propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. Illumination estimation is considered an important component of many higher level computer vision tasks such as object recognition and tracking It has been extensively studied in order to develop reliable color constancy systems which can achieve illumination invariance to some extent [1], [3]. When a person stands in a room lit by a colorful light, the Human Visual System (HVS) unconsciously removes the lightening effects and the colors are perceived as if they were illuminated by a neutral, white light. While this ability is very natural for the HVS, mimicking the same ability in a computer vision system is a challenging and under-constrained problem.
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