Image semantic extraction method applied in electronic guidance system

Abstract

The invention discloses an image semantic extraction method applied in an electronic guidance system. Image semantics can be rapidly and accurately extracted, and requirements of the electronic guidance system based on computer vision for image understanding can be satisfied. The image semantic extraction method comprises: 1) a training stage: building a training image library T, and extracting ascale invariant feature transform (SIFT) features of images in T to form a set F; constructing a dictionary V through a multi-stage close relationship propagation algorithm; mapping F onto Fv throughthe dictionary V and sparse codes; and training a linear support vector machine (LSVM) through Fv; and 2) a use stage: dividing a collected image Iq into 10 equal sub-blocks overlapped partially; extracting features of each sub-block through the feature extraction method; classifying the features of each sub-block through the LSVM to obtain corresponding semantic information; and determining a semantic tag of the image Iq according to the semantic information of the sub-blocks, and converting the semantic tag into voice output.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, relates to image understanding and pattern recognition, and is an image semantic extraction algorithm applied to a blind guide system. The algorithm has a high recognition rate and can meet the real-time requirements of the blind guide system. Background technique [0002] For a long time, the damage or loss of vision has brought great inconvenience to the lives of patients, and the problem of walking is a major problem in the life of the visually impaired. In daily life, they need the help of traditional guide methods such as crutches or guide dogs to walk independently from one place to another. Although many electronic blind-guiding systems have appeared in recent years, they are the same as traditional blind-guiding methods. Obstacles cannot perceive the relevant information of the surrounding environment, such as whether there are pedestrians, houses or vehicles around. T...

AI Technical Summary

Problems solved by technology

However, these features are often applicable to some specific image objects. If they are applied to real life, it will lead to low accuracy of image semantic extraction.

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