Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transferring color to greyscale images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Colorization using optimization
ACM SIGGRAPH 2004 Papers
An adaptive edge detection based colorization algorithm and its applications
Proceedings of the 13th annual ACM international conference on Multimedia
ACM SIGGRAPH 2006 Papers
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH Asia 2008 papers
Automatic Image Colorization Via Multimodal Predictions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic colorization with internet images
Proceedings of the 2011 SIGGRAPH Asia Conference
Fast image and video colorization using chrominance blending
IEEE Transactions on Image Processing
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
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We present a new example-based method to colorize a gray image. As input, the user needs only to supply a reference color image which is semantically similar to the target image. We extract features from these images at the resolution of superpixels, and exploit these features to guide the colorization process. Our use of a superpixel representation speeds up the colorization process. More importantly, it also empowers the colorizations to exhibit a much higher extent of spatial consistency in the colorization as compared to that using independent pixels. We adopt a fast cascade feature matching scheme to automatically find correspondences between superpixels of the reference and target images. Each correspondence is assigned a confidence based on the feature matching costs computed at different steps in the cascade, and high confidence correspondences are used to assign an initial set of chromatic values to the target superpixels. To further enforce the spatial coherence of these initial color assignments, we develop an image space voting framework which draws evidence from neighboring superpixels to identify and to correct invalid color assignments. Experimental results and user study on a broad range of images demonstrate that our method with a fixed set of parameters yields better colorization results as compared to existing methods.