Bozhi Liu

Bozhi Liu

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portfolio

publications

Illuminant classification based on random forest

Published in 14th IAPR International Conference on Machine Vision Applications (MVA), 2015

We present a novel machine learning/pattern recognition based colour constancy method. We cast colour constancy as an illumination source recognition problem, and have developed an effective and efficient random forest based classification technique for inferring the class of illumination source of an image. In an opponent colour space, we have developed a binary image representation feature that is somewhat insensitive to image contents for building the random forest classifier that infers the likely class of the illumination source of the image. The binary image feature and the tree structure of the recognition system are intrinsically efficient. We present results on colour constancy benchmark data sets and show that our new technique outperforms state of the art techniques.

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A classification method for estimating the illuminant of an image

Published in Visual Communications and Image Processing (VCIP), 2016

Identifying the light source of an image is important for image processing tasks such as colour correction and white point balancing. This is also known as colour constancy in computer vision. This paper presents a novel clustering classification colour constancy framework (the 4C method). Based on the assumption that similar illuminants will result in similar white point colours, we first use a clustering algorithm to group similar white point colours of the training samples into the same cluster. We then treat the images in the same cluster as belonging to the same illumination source and each cluster as one class of illuminants. The colour constancy problem, i.e., that of estimating the unknown illuminant of an image, becomes that of identifying which illuminant class (cluster) the images illuminant falling into. To achieve this, we derive an effective colour feature representation of the image and use a general classification algorithm to classify the image into one of the illuminant classes (clusters). We present experimental results on publicly available testing datasets and show that our new method is competitive to state of the art.

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End-to-end illuminant estimation based on deep metric learning

Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image. Such approaches might suffer heavily from being sensitive to the variation of image content. To overcome this problem, we introduce a deep metric learning approach named Illuminant-Guided Triplet Network (IGTN) to color constancy. IGTN generates an Illuminant Consistent and Discriminative Feature (ICDF) for achieving robust and accurate illuminant color estimation. ICDF is composed of semantic and color features based on a learnable color histogram scheme. In the ICDF space, regardless of the similarities of their contents, images taken under the same or similar illuminants are placed close to each other and at the same time images taken under different illuminants are placed far apart. We also adopt an end-to-end training strategy to simultaneously group image features and estimate illuminant value, and thus our approach does not have to classify illuminant in a separate module. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches. Furthermore, we demonstrate that our method is less sensitive to image appearances, and can achieve more robust and consistent results than other methods on a High Dynamic Range dataset.

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Tone mapping high dynamic range images based on region-adaptive self-supervised deep learning

Published in Signal Processing: Image Communication, 2022

This paper presents a region-adaptive self-supervised deep learning (RASSDL) technique for high dynamic range (HDR) image tone mapping. The RASSDL tone mapping operator (TMO) is a convolutional neural network (CNN) trained on local image regions that can seamlessly tone map images of arbitrary sizes. The training of RASSDL TMO is through the design of a self-supervising target that automatically adapts to the local image regions based on their information contents. The self-supervising target is designed to ensure the tone-mapped output achieves a balance between preserving the relative contrast of the original scene and the visibilities of the fine details to achieve faithful reproduction of the HDR scene. Distinguishing from many existing TMOs that require manual tuning of parameters, RASSDL is parameter-free and completely automatic. Experimental results demonstrate that RASSDL TMO can achieve state-of-the-art performance in terms of preserving overall contrasts, revealing fine details, and being free from visual artifacts.

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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