Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

An Overview of Hand Gestures Recognition

Abstract: In recent years,with the development of human-computer interaction and computer vision,gesture recognition research has made tremendous progress.In this paper,we summarize the research and development status of the gesture recognition.First,the development process of the gesture recognition is introduced.Furthermore,several gesture recognition technologies are based on the general flow of the gesture recognition are proposed.The conclusion and the future scope are given eventually.

Composite Sketch Recognition Using Multi-scale Hog Features and Semantic Attributes

Abstract: Composite sketch recognition belongs to heterogeneous face recognition research, which is of great important in the field of criminal investigation. Because composite face sketch and photo belong to different modalities, robust representation of face feature cross different modalities is the key to recognition. Considering that composite sketch lacks texture details in some area, using texture features only may result in low recognition accuracy, this paper proposes a composite sketch recognition algorithm based on multi-scale Hog features and semantic attributes. Firstly, the global Hog features of the face and the local Hog features of each face component are extracted to represent the contour and detail features. Then the global and detail features are fused according to their importance at score level. Finally, semantic attributes are employed to reorder the matching results. The proposed algorithm is validated on PRIP-VSGC database and UoM-SGFS database, and achieves rank 10 identification accuracy of 88.6% and 96.7% respectively, which demonstrates that the proposed method outperforms other state-of-the-art methods.

Using Error Recovery Techniques to Improve Sketch Recognition Accuracy

Abstract: Sketching is an activity that produces informal documents containing hand-drawn shapes highly variable and ambiguous. In this paper we present a diagrammatic sketch recognizer that is able to cope with the recognition of in-accurate hand-drawn symbols by exploiting error recovery techniques as developed for programming language compilers. The error recovery algorithms are able to interact with recognizers automatically generated from grammar specifications in order to obtain the information on missing or misrecognized strokes.

Teach machine to learn: hand-drawn multi-symbol sketch recognition in one-shot

Abstract: The ability to sequentially learn from few examples and re-utilize previous knowledge is an important milestone on the path to artificial general intelligence. In this paper, we propose Teach Machine to Learn (TML), a few-shot learning model for hand-drawn multi-symbol sketch recognition. The model decomposes multi-symbol sketch into stroke primitives and then explains the observed sequences in a bayesian criterion. A Bidirectional Long Short Term Memory (BiLSTM) encoder is employed for stroke primitives encoding. Meanwhile, a probabilistic Hidden Markov Model (HMM) is constructed for complete sketch inference and recognition. The challenging task of hand-drawn multi-symbol sketch recognition is implemented on two public datasets. The comparative results indicate that the proposed method outperforms the currently booming image-based deep models in recognition accuracy. Furthermore, our method is capable to continuously learn new concepts even in one-shot. The codes are currently available in https://github.com/chongyupan/Teach-Machine-to-Learn.