Sketch-based retrieval of drawings using spatial proximity

Sousa, Pedro; Fonseca, Manuel J.

doi:10.1016/j.jvlc.2009.12.001

Cited by 47 publications

(35 citation statements)

References 11 publications

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“…Sketch query based retrieval system has been used for searching trademarks [8,9], technique drawings [10,11], documents [12], hand drawn image set or clip arts [13,14,15]. These works often consider sketches as a combination of strokes or geometry units and the relation between them are used to represent the shape for matching [16,17,18].…”

Section: Related Workmentioning

confidence: 99%

A performance evaluation of gradient field HOG descriptor for sketch based image retrieval

Collomosse

2013

Computer Vision and Image Understanding

335

285

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We present an image retrieval system for the interactive search of photo collections using free-hand sketches depicting shape. We describe Gradient Field HOG (GF-HOG); an adapted form of the HOG descriptor suitable for sketch based image retrieval (SBIR). We incorporate GF-HOG into a Bag of Visual Words (BoVW) retrieval framework, and demonstrate how this combination may be harnessed both for robust SBIR, and for localizing sketched objects within an image. We evaluate over a large Flickr sourced dataset comprising 33 shape categories, using queries from 10 non-expert sketchers. We compare GF-HOG against state-of-the-art descriptors with common distance measures and language models for image retrieval, and explore how affine deformation of the sketch impacts search performance. GF-HOG is shown to consistently outperform retrieval versus SIFT, multi-resolution HOG, Self Similarity, Shape Context and Structure Tensor. Further, we incorporate semantic keywords in to our GF-HOG system to enable the use of annotated sketches for image search. A novel graph-based measure of semantic similarity is proposed and two applications explored: semantic sketch based image retrieval and a semantic photo montage.

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Section: Related Workmentioning

confidence: 99%

A performance evaluation of gradient field HOG descriptor for sketch based image retrieval

Collomosse

2013

Computer Vision and Image Understanding

335

285

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“…The general concept of a structured feature representation has been adopted elsewhere including scene category recognition [11], landmark images retrieval [3] and sketch-based retrieval of drawings [7,19]. However, most of these structure encoding methods are fairly specific to the problem domains they were designed to address, therefore are not readily applicable to the problem of representing sketches.…”

Section: Related Workmentioning

confidence: 99%

“…There are several methods proposed for structured representations of sketches, in which topological relationships between sketch parts were exploited for improving matching accuracy [7,19]. However, such schemes are restricted to a certain type of sketches, namely technical drawings (e.g.…”

Section: Related Workmentioning

confidence: 99%

Sketch Recognition by Ensemble Matching of Structured Features

Li¹,

Song²,

Gong³

2013

Procedings of the British Machine Vision Conference 2013

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Sketch recognition aims to automatically classify human hand sketches of objects into known categories. This has become increasingly a desirable capability due to recent advances in human computer interaction on portable devices. The problem is nontrivial because of the sparse and abstract nature of hand drawings as compared to photographic images of objects, compounded by a highly variable degree of details in human sketches. To this end, we present a method for the representation and matching of sketches by exploiting not only local features but also global structures of sketches, through a star graph based ensemble matching strategy. Different local feature representations were evaluated using the star graph model to demonstrate the effectiveness of the ensemble matching of structured features. We further show that by encapsulating holistic structure matching and learned bag-of-features models into a single framework, notable recognition performance improvement over the state-of-the-art can be observed. Extensive comparative experiments were carried out using the currently largest sketch dataset released by Eitz et al. [15], with over 20,000 sketches of 250 object categories generated by AMT (Amazon Mechanical Turk) crowd-sourcing.

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“…Fonseca et al [6] proposed a sketch-based system for retrieving complex drawings by organizing the hierarchical structure of the shape according to topology and geometry. Sousa and Fonseca [12] also presented a scheme that retrieves similar images using topological proximity. Fonseca et al [13] proposed a sketch-based retrieval for simple drawings such as clipart images.…”

Section: Related Workmentioning

confidence: 99%

“…They applied Fourier transform to the images in the database and the user-created sketch image and executed image matching using the distribution of angle space. Sousa and Fonseca [12] combined text information and saliency estimated from the images in database. The text information specified by users are compared with the saliency precomputed.…”

Section: Related Workmentioning

confidence: 99%