Topic
Sketch recognition
About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.
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21 May 2014
TL;DR: This paper proposes a hierarchical structure Category-Separating Strategy for branded handbag recognition which is the first attempt to address the fine-grained object recognition on branded handbags.
Abstract: In recent years, computer vision community has devoted efforts on the recognition of basic-level categories. On the other hand, finegrained object recognition which targets at recognizing objects belonging to the same basic-level class, is a more challenging problem and receives an increasing attention during recent years. In this paper, we propose a hierarchical structure Category-Separating Strategy for branded handbag recognition which is the first attempt to address the fine-grained object recognition on branded handbags. Experimental results on a newly constructed dataset are provided to show the effectiveness of the proposed methodology.
3 citations
Journal Article•
TL;DR: In this paper, a nested recursive optimization process is designed by means of dynamic programming to do stroke segmentation and symbol recognition cooperatively by minimizing the fitting errors between inputting patterns and templates.
Abstract: This paper presents a solution for online composite sketchy shape recognition. The kernel of the strategy treats both stroke segmentation and sketch recognition as an optimization problem of fitting to a template. A nested recursive optimization process is then designed by means of dynamic programming to do stroke segmentation and symbol recognition cooperatively by minimizing the fitting errors between inputting patterns and templates. Experimental results prove the effectiveness of the proposed method.
3 citations
02 Dec 2018
TL;DR: In this article, a deep metric learning loss was proposed to minimize the Bayesian risk of misclassification for each mini-batch during training. But the loss was not applied to the task of hand-drawn sketch recognition.
Abstract: In this paper, we address the problem of hand-drawn sketch recognition. Inspired by the Bayesian decision theory, we present a deep metric learning loss with the objective to minimize the Bayesian risk of misclassification. We estimate this risk for every mini-batch during training, and learn robust deep embeddings by backpropagating it to a deep neural network in an end-to-end trainable paradigm. Our learnt embeddings are discriminative and robust despite of intra-class variations and inter-class similarities naturally present in hand-drawn sketch images. Outperforming the state of the art on sketch recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and TU-Berlin-160 benchmarks respectively.
3 citations
01 Jan 2009
TL;DR: This paper introduces a control point based editing approach called RingEdit, which differs from other sketch editors in that the user actually draws their own control points on the sketch, rather than relying on control points generated by the recognition system.
Abstract: Editing a sketch should be one of the essential features provided by sketch recognition systems to allow people to modify what they have drawn, without having to delete and redraw shapes. This paper introduces a control point based editing approach we call RingEdit. RingEdit differs from other sketch editors in that the user actually draws their own control points on the sketch, rather than relying on control points generated by the recognition system. It provides modes that allow moving, rotating, scaling, and bending on both the shape level and stroke level. RingEdit shows great editing capabilities. Author Keywords Sketch recognition, sketch editing, control points.
3 citations
03 Nov 2014
TL;DR: SmartVisio is a real-time sketch recognition system based on Visio, to recognize hand-drawn flowchart/diagram with flexible interactions, and a novel symbol recognition algorithm is proposed to better recognize or differentiate some visually similar shapes.
Abstract: In this work, we introduce the SmartVisio system for interactive hand-drawn shape/diagram recognition. Different from existing work, SmartVisio is a real-time sketch recognition system based on Visio, to recognize hand-drawn flowchart/diagram with flexible interactions. This system enables a user to draw shapes or diagrams on the Visio interface, and then the hand-drawn shapes are automatically converted to formal shapes in real-time. To satisfy the interaction needs from common users, we propose an algorithm to detect a user's correction and editing during drawing, and then recognize in real time. We also propose a novel symbol recognition algorithm to better recognize or differentiate some visually similar shapes. By enabling users' natural correction/editing on various shapes, our system makes flowchart/diagram production much more natural and easier.
3 citations