SketchFormer: transformer-based approach for sketch recognition using vector images

Sketches have been employed since the ancient era of cave paintings for simple illustrations to represent real-world entities and communication. The abstract nature and varied artistic styling make automatic recognition of these drawings more challenging than other areas of image classification. Mor...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2021-03, Vol.80 (6), p.9075-9091
Main Authors: Parihar, Anil Singh, Jain, Gaurav, Chopra, Shivang, Chopra, Suransh
Format: Article
Language:English
Subjects:
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Image classification
Multimedia Information Systems
Object recognition
Qualitative analysis
Sketches
Special Purpose and Application-Based Systems
Styling
Transformers
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sketches have been employed since the ancient era of cave paintings for simple illustrations to represent real-world entities and communication. The abstract nature and varied artistic styling make automatic recognition of these drawings more challenging than other areas of image classification. Moreover, the representation of sketches as a sequence of strokes instead of raster images introduces them at the correct abstract level. However, dealing with images as a sequence of small information makes it challenging. In this paper, we propose a Transformer-based network, dubbed as AttentiveNet, for sketch recognition. This architecture incorporates ordinal information to perform the classification task in real-time through vector images. We employ the proposed model to isolate the discriminating strokes of each doodle using the attention mechanism of Transformers and perform an in-depth qualitative analysis of the isolated strokes for classification of the sketch. Experimental evaluation validates that the proposed network performs favorably against state-of-the-art techniques.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09837-y