#Keyshape meaning how to#

Searching is a necessary tool for managing and navigating the massive amounts of data available in today’s information age. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. Extensive experiments show that the proposed representations are highly discriminative and lead to large improvements over the state of the arts.ĭigitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. An improved dictionary learning algorithm is also leveraged in HMSC to reduce over-fitting to common but trivial patterns. Then, based on this representation, to discovery more complex patterns in a sketch, a Hybrid Multilayer Sparse Coding (HMSC) model is proposed to learn mid-level representations. First, to capture saliency patterns of different scales and spatial arrangements, a Gabor-based low-level representation is proposed.

#Keyshape meaning how to#

In this work, we study how to build effective representations for sketch recognition. However, different from natural images, the hand-drawn sketches are often highly abstract, with sparse visual information and large intra-class variance, making the problem more challenging.

Extensive experiments on different applications show the effectiveness of the proposed topic model and the recognition system.Īs the popularity of touch-screen devices, understanding a user's hand-drawn sketch has become an increasingly important research topic in artificial intelligence and computer vision. Besides sketch recognition, the proposed topic model can also be used for related applications such as sketch tagging, image tagging, and sketch-based image search. To alleviate the problems of intra-class shape variation and inter-class shape ambiguity in this unconstrained situation, a query-adaptive shape topic model is proposed to mine object topics and shape topics related to the sketch, in which, multiple layers of information such as sketch, object, shape, image, and semantic labels are modeled in a generative process. To increase the recognition coverage, a web-scale clipart image collection is leveraged as the knowledge base of the recognition system. Different from existing work, we target at developing a general sketch recognition system, to recognize any semantically meaningful object that a child can recognize.

Due to large intra-class variations presented in hand-drawn sketches, most of existing work was limited to a particular domain or limited pre-defined classes. In this work, we study the problem of hand-drawn sketch recognition.