Sketch retrieval via dense stroke features

Chao Ma, Xiaokang Yang, Chongyang Zhang, Xiang Ruan, Ming Hsuan Yang

Research output: Contribution to conferencePaper

14 Citations (Scopus)

Abstract

Sketch retrieval aims at retrieving most similar sketches from a large database based on one hand-drawn query. Successful retrieval hinges on an effective representation of sketch images and an efficient search method. In this paper, we propose a representation scheme which takes sketch strokes into account with local features, thereby facilitating efficient retrieval with codebooks. Stroke features are detected via densely sampled points on stroke lines from which local gradients are further enhanced and described by a quantized histogram of gradients. A codebook is organized in a hierarchical vocabulary tree, which maintains structural information of visual words and enables efficient retrieval in sub-linear time. Experimental results on three data sets demonstrate the merits of the proposed algorithm for effective and efficient sketch retrieval.

Original languageEnglish
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 24th British Machine Vision Conference, BMVC 2013 - Bristol, United Kingdom
Duration: 2013 Sep 92013 Sep 13

Conference

Conference2013 24th British Machine Vision Conference, BMVC 2013
CountryUnited Kingdom
CityBristol
Period13/9/913/9/13

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Ma, C., Yang, X., Zhang, C., Ruan, X., & Yang, M. H. (2013). Sketch retrieval via dense stroke features. Paper presented at 2013 24th British Machine Vision Conference, BMVC 2013, Bristol, United Kingdom. https://doi.org/10.5244/C.27.65