Sketch-based image retrieval on mobile devices using compact hash bits

Tseng, Kai-Yu; Lin, Yen-Liang; Chen, Y-H.; Hsu, Winston H.

doi:10.1145/2393347.2396345

Cited by 31 publications

(12 citation statements)

References 6 publications

(6 reference statements)

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“…However, this offers limited invariance to changes in position, scale or orientation. Kai-Yu Tseng et al [12] focused on the idea of SBIR for mobile devices. This approach also splits the image edges in six orientations.…”

Section: Related Workmentioning

confidence: 99%

A Methodology for Sketch based Image Retrieval based on Score level Fusion

Jhansi¹,

Reddy²

2015

IJCA

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Retrieving sketches to match with a hand drawn sketch query is highly desired feature. This paper proposes a novel methodology for efficient retrieval of sketch based images. This system extracts features from the query sketch, HOG and GMM features are used and these features are combined using score level fusion which can match user drawn sketch with database sketches efficiently. The methodology is tested on bench mark images and the performance evaluation is carried out using metrics like Precession and Recall. The results derived are tested for efficiency against models based on HOG and GMM.

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Section: Related Workmentioning

confidence: 99%

A Methodology for Sketch based Image Retrieval based on Score level Fusion

Jhansi¹,

Reddy²

2015

IJCA

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“…The scope of the retrieval is therefore limited. Several methods have tackled the sketch-based retrieval of naturalistic images, where queries are sketches drawn by users [15,16,1,17,18,19,20,21].…”

Section: Related Workmentioning

confidence: 99%

Sketch2Manga: Sketch-based manga retrieval

Matsui

Aizawa

2014

2014 IEEE International Conference on Image Processing (ICIP)

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We propose a sketch-based method for manga image retrieval, in which users draw sketches via a Web browser that enables the automatic retrieval of similar images from a database of manga titles. The characteristics of manga images are different from those of naturalistic images. Despite the widespread attention given to content-based image retrieval systems, the question of how to retrieve manga images effectively has been little studied. We propose a fine multi-scale edge orientation histogram (FMEOH) whereby a number of differently sized squares on a page can be indexed efficiently. Our experimental results show that FMEOH can achieve greater accuracy than a state-of-the-art sketch-based retrieval method [1].

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“…However, significant advances in efficiency were only made recently (e.g., [1]), making practical applications possible. Among the existing SBIR proposals, we focus on [2], [3], [1], [4], [5], which we consider particularly relevant.…”

Section: A Related Workmentioning

confidence: 99%

“…A similar approach in terms of memory cost saving is presented in [5]. This SBIR application is also based on the approach [1], and the authors focused on the idea of SBIR for mobile devices.…”

Section: A Related Workmentioning

confidence: 99%

Sketch-Finder: Efficient and Effective Sketch-Based Retrieval for Large Image Collections

Filho

Arajujo

Crucianu

et al. 2013

2013 XXVI Conference on Graphics, Patterns and Images

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Fig. 1. Sketch-Finder (SF) vs. Edgel-Index (EI) -results on the ImageNet dataset.Abstract-Among various image retrieval approaches, the use of sketches lets one express a precise visual query with simple and widespread means. The challenge consists in finding a content representation that allows you to effectively compare sketches and images, while supporting efficient retrieval in order to make the system scalable. We put forward a sketch-based image retrieval solution where sketches and natural image contours are represented and compared in the wavelet domain. The relevant information regarding query sketches and image content has, thus, a compact representation that can be readily employed by an efficient index for retrieval by similarity. Furthermore, with this solution, the balance between effectiveness and efficiency can be easily modified in order to adapt to the available resources. A comparative evaluation with a state-of-the-art method on the Paris dataset and a subset with 535K images of the ImageNet dataset shows that our solution can preserve effectiveness while being more than one order of magnitude faster.

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Sketch-based image retrieval on mobile devices using compact hash bits

Cited by 31 publications

References 6 publications

A Methodology for Sketch based Image Retrieval based on Score level Fusion

A Methodology for Sketch based Image Retrieval based on Score level Fusion

Sketch2Manga: Sketch-based manga retrieval

Sketch-Finder: Efficient and Effective Sketch-Based Retrieval for Large Image Collections

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