Color Constancy Convolutional Autoencoder

In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table

TWO-ILLUMINANT ESTIMATION AND USER-PREFERRED CORRECTION FOR IMAGE COLOR CONSTANCY

Master'sMASTER OF SCIENC

Probabilistic Color Image Classifier Based on Volumetric Robust Features

Need of more sophisticated methods to handle color images becomes higher due to the usage size and volume of images To retrieve and index the color images there must be a proper and efficient indexing and classification method to reduce the processing time false indexing and increase the efficiency of classification and grouping We propose a new probabilistic model for the classification of color images using volumetric robust features which represents the color and intensity values of an region The image has been split into number of images using box methods to generate integral image The generated integral image is used to compute the interest point and the interest point represent the volumetric feature of an integral image With the set of interest points computed for a source image we compute the probability value of other set of interest points trained for each class to come up with the higher probability to identify the class of the input image The proposed method has higher efficiency and evaluated with 2000 images as data set where 70 has been used for training and 30 as test se

Semantik renk değişmezliği

Color constancy aims to perceive the actual color of an object, disregarding the effectof the light source. Recent works showed that utilizing the semantic information inan image enhances the performance of the computational color constancy methods.Considering the recent success of the segmentation methods and the increased numberof labeled images, we propose a color constancy method that combines individualilluminant estimations of detected objects which are computed using the classes of theobjects and their associated colors. Then we introduce a weighting system that valuesthe applicability of the object classes to the color constancy problem. Lastly, weintroduce another metric expressing the detected object and how well it fits the learnedmodel of its class. Finally, we evaluate our proposed method on a popular colorconstancy dataset, confirming that each weight addition enhances the performanceof the global illuminant estimation. Experimental results show promising results,outperforming the conventional methods while competing with the state of the artmethods.--M.S. - Master of Scienc