Semantic colorization with internet images

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

doi:10.1145/2024156.2024190

Cited by 87 publications

(62 citation statements)

References 24 publications

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“…More recent research also tried to produce high quality saliency maps in a filtering based framework [46], using efficient data representation [12], or consider hierarchical structures [55]. Such salient object segmentation for simple images achieved great success in image scene analysis [15,58], content aware image editing [13,56,60], and it can be used as a cheap tool to process large number of Internet images or build robust applications [7,8,16,31,34,35] by automatically selecting good results [10,11]. However, these approaches are less likely to work for complicated images where many objects are presented and they are rarely dominant (e.g.…”

Section: Related Workmentioning

confidence: 99%

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Cheng

Zhang

Lin

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

825

642

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Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with welldefined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure.We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1,000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5%

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Section: Related Workmentioning

confidence: 99%

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Cheng

Zhang

Lin

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

825

642

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show abstract

“…To take advantages of the abundant image resources on the internet, many works have been done [17], such as for image editing by semantic colorization [7] and object manipulation in images [13]. Here, an important task is to retrieve suitable images from the internet.…”

Section: Image Retrievalmentioning

confidence: 99%

Effective structure restoration for image completion using internet resources

Hua

Wang

2015

Vis Comput

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Structure restoration plays an important role in image completion. Though much progress has been made, existing techniques are ineffective to restore plausible complex structures, due to the high complexity of structure registration between neighboring regions. We address this challenge by taking two measures. The first is to get the images whose structures are potential to be well merged with the structures around the hole. This can be achieved by effectively retrieving the mass images on the internet, which provide enough candidates. The second is to coherently blend the retrieved structures with the structures around the hole, which is by suppressing the connection differences between these structures. Experimental results show that we can easily complete images, especially with complex structures or for filling large holes.

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“…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 retrieval [12,27,28,30], and internet visual media processing [11,33,34,40]. Recently, significant success has been reported in saliency-based image segmentation producing near ground-truth performance on simple images ( [1,13,15,41] and references therein).…”

Section: Introductionmentioning

confidence: 99%

SalientShape: group saliency in image collections

et al. 2013

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Efficiently identifying salient objects in large image collections is essential for many applications including image retrieval, surveillance, image annotation, and object recognition. We propose a simple, fast, and effective algorithm for locating and segmenting salient objects by analysing image collections. As a key novelty, we introduce group saliency to achieve superior unsupervised salient object segmentation by extracting salient objects (in collections of pre-filtered images) that maximize between-image similarities and within-image distinctness. To evaluate our method, we construct a large benchmark dataset consisting of 15 K images across multiple categories with 6000+ pixel-accurate ground truth annotations for salient object regions where applicable. In all our tests, group saliency consistently outperforms state-of-the-art single-image saliency algorithms, resulting in both higher precision and better recall. Our algorithm successfully handles image collections, of an order larger than any existing benchmark datasets, consisting of diverse and heterogeneous images from various internet sources.

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Semantic colorization with internet images

Cited by 87 publications

References 24 publications

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Effective structure restoration for image completion using internet resources

SalientShape: group saliency in image collections

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