Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors

By Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur, Marc Alexa. 2011

465 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Education, History, Information Retrieval & Search

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Citations in the corpus

A Zero-Shot Framework for Sketch-based Image Retrieval (2018, background): “This convenience in expressing a visual query has led to the emergence of Sketch-based image retrieval (SBIR) as an active area of research [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].”
SalientShape: group saliency in image collections (2013, methodology): “Among top 50 Among top 100 Flickr Our SHoG Flickr Our SHoG Bottle 58 98 84 60 94 82 Butterfly 28 54 36 28 50 40 Cake 56 84 78 55 81 79 C a t t l e 4 8 9 4 1 4 3 7 8 1 1 2 C o f f e em u g 5 8 9 4 8 2 5 1 9 0 7 8 C o w 5 2 9 4 5 4 5 4 9 2 5 5 Crow 36 96 76 38 86 61 D o gj u m p 5 6 9 2 7 4 5 5 8 5 7 ”
Sketch-Guided Object Localization in Natural Images (2020, methodology): “two broad categories- (a) classical methods which use hand-crafted features, for example, SIFT or gradient-eld histogram of gradients along with a bag-of-words representation of sketches as shown in [5,14], (b) deep methods which utilize cross-modal deep learning techniques by incorporating ranking ”
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation (2020, methodology): “Color [37] 10,000 pedestrian 24 LabelMeFacade [38] 395 building 25 UT Zappos50K [39] 10,000 shoes 26 TRANCOS [40] 1,244 traffic 27 FGVC [40] 10,000 aeroplane 28 Mall Dataset [41] 2,000 mall 29 Chars74K [42] 7,705 character 30 DomainNet [8] 10,000 painting 31 Paris Dataset [43] 3,187 street 32 Domain”
Generalising Fine-Grained Sketch-Based Image Retrieval (2019, background): “Fine-grained SBIR Most earlier SBIR studies [5, 10, 4, 14, 38, 7, 20] focus on category-level cross-domain matching.”
Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval (2019, background): “Such amateur sketches are very different from the ones currently studied by existing datasets, which are either too photo-realistic [6] or produced by recollection of a reference images [25] (Figure 1 offers a comparative example).”
Zero-Shot Learning via Visual Abstraction (2014, background): “Through a pilot study, just as in [6], we found two seconds to be sufficient for people to capture the more salient aspects of the image.”
How do humans sketch objects? (2012, methodology): “This results in a representation that is closely related to the one used for SIFT [Lowe 2004] but stores orientations only [Eitz et al. 2011a].”
Fine-Grained Image Retrieval: the Text/Sketch Input Dilemma (2017, background): “Fine-grained Sketch-based Image Retrieval The traditional category-level SBIR problem has been well studied [2, 9, 15, 24, 27, 35].”
Cross-Domain Adversarial Feature Learning for Sketch Re-identification (2018, background): “There are many works [7, 8, 12, 31] focusing on category-level sketch classification and sketch-to-photo retrieval.”
Indexing billions of images for sketch-based retrieval (2013, methodology): “(a) Sketch classi.cation accuracy of CM[2], OCM[13], SHoG[5], Shape Context (SC)[1], and Gist[12].”
Learning Aligned Cross-Modal Representations from Weakly Aligned Data (2016, unknown): “[8] focuses on recovering semantically related natural images to a given sketch query and [41] uses query sketches to recover 3D shapes.”
A performance evaluation of gradient field HOG descriptor for sketch based image retrieval (2013, methodology): “This practice was referred to as EDGE-HOG in [6] and S-HOG in [38].”
Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval (2017, background): “Fine-grained SBIR Most existing SBIR works [2, 3, 7, 13, 25, 29] focus on category-level sketch-to-photo retrieval.”
Deep Learning for Free-Hand Sketch: A Survey (2020, background): “Therefore, in recent ten years, a number of sketch datasets [55], [56], [57], [58], [59] are proposed.”