Semantic colorization with internet images

By Alex Yong Sang Chia, Shaojie Zhuo, Raj Kumar Gupta, Yu-Wing Tai, Siu-Yeung Cho, Ping Tan, Stephen Lin. 2011

268 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Colorful Image Colorization (2016, methodology): “Liu et al. [8] and Chia et al. [9] propose methods which utilize reference images gathered from the Internet.”
Learning Representations for Automatic Colorization (2016, methodology): “Mapping between source and target is established automatically, using correspondences between local descriptors [3, 26, 41], or in combination with manual intervention [6, 19].”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “It is an unsupervised task, but also one that is semantic-aware[6].”
Real-time user-guided image colorization with learned deep priors (2017, methodology): “Early semi-automatic methods (Chia et al. 2011; Gupta et al. 2012; Irony et al. 2005; Liu et al. 2008; Welsh et al. 2002) utilize an example-based approach that transfers color statistics from a reference image or multiple images (Liu et al. 2014; Mori-moto et al. 2009) to the input grayscale image…”
PixColor: Pixel Recursive Colorization (2017, background): “Early approaches to colorization relied on some amount of human effort, either to identify a relevant source color image from which the colors could be transferred [3, 6, 13, 16, 24, 25, 28, 33, 39], or to get a rough coloring from a human annotator to serve as a set of "hints" [11, 14, 21, 22, 26, ”
SalientShape: group saliency in image collections (2013, background): “Determining characteristic or salient regions of images allows transitioning from low-level pixels to more meaningful high-level regions, and thus form an essential step for many computer graphics and computer vision applications, including interactive image editing [14, 16, 41, 51, 53], image retri”
Deep Colorization (2015, methodology): “To release the users’ burden of ﬁnding a suitable image, Liu et al.[11] and Chia et al. [8] utilize the massive image data on the Internet.”
Deep exemplar-based colorization (2018, methodology): “In the second paradigm [Welsh et al. 2002; Irony et al. 2005; Tai et al. 2005; Charpiat et al. 2008; Liu et al. 2008; Chia et al. 2011; Gupta et al. 2012; Bugeau et al. 2014], a color reference image similar to the grayscale image is given to facilitate the process.”
Image colorization using similar images (2012, methodology): “From the figure, we can see that the algorithms [12] (which requires exact object instances to be present in both reference and gray images) and [3] (which requires segmentation masks of foreground objects) are able to transfer the exact color values at few pixel locations due to their use of spatia”
Salient Object Detection: A Benchmark (2015, background): “[36], [37], image quality assessment [38]–[41], image segmentation [42]–[45], content-based image retrieval and image collection browsing [46]–[49], image editing and manipulating [50]–[53], visual tracking [54]–[60], object discovery [61], [62], and human-robot interaction [63]–[65].”
Global Contrast Based Salient Region Detection (2015, background): “object of interest regions is powerful to explore this big data for image composition [82], semantic colorization [83], information discovery [84], [85], image”
Attentive Systems: A Survey (2017, background): “As image colorization can bring a grayscale photo to life, Chia et al. (2011) propose amethod that utilizes internet photos and image filtering to minimize user effort and facilitate accurate color transfer.”
Salient object detection: A survey (2014, methodology): “t aware resizing [59] (b) Image collage [191] (c) View selection [192] (d) Unsupervised learning [193] (e) Mosaic [194] (f) Image montage [195] (g) Object manipulation [196] (h) Semantic colorization [197] Fig. 9. Sample applications of salient object detection. Images are reproduced from correspond”