Object recognition method based on semantic feature extraction and matching

Abstract

The invention provides an object recognition method based on semantic feature extraction and matching and belongs to the field of information retrieval. The object recognition method based on semantic feature extraction and matching includes semantic feature extraction and semantic feature matching. The semantic feature extraction includes firstly extracting SIFT (Scale Invariant Feature Transform) feature points of training images of a class of objects, then performing spatial clustering on the SIFT feature points through k- means clustering, deciding a plurality of efficient points in every space class through a decision-making mechanism based on kernel function, and finally training the efficient points in every space class through a support vector machine classifier; a visual word with semantic features is trained from every space class, and finally a visual vocabulary describing the semantic features of a class of objects is extracted. The semantic feature matching includes firstly extracting SIFT feature points of an image of an object to be detected as the semantic description of the object to be detected, then using the support vector machine classifier for matching and classifying the semantic description of the object to be detected and visual vocabularies of classes of objects, and finally counting a histogram of the visual vocabulary of the object to be detected for determining the class of the object to be detected.

Description

technical field [0001] The invention belongs to the field of information retrieval, and in particular relates to an object recognition method based on semantic feature extraction and matching. Background technique [0002] The essence of object recognition is to establish a computing system that can identify the object category of interest in the image, which has a wide range of application requirements in real life, and has very high application value and research significance. In recent years, with the continuous maturity of pattern classification technology and the continuous development of artificial intelligence, object recognition technology based on semantic feature extraction has gradually been favored by scholars. The semantic feature of an object is to extract the local features of a class of objects, and then transform the local features into semantic information describing a class of objects according to certain processing criteria, forming a semantic feature mod...

AI Technical Summary

Problems solved by technology

However, only using a single feature point in the cluster center as a description of a class of feature points does not make full use of local features, nor does it make full use of the semantic information generated after clustering

A single cluster center loses a lot of semantic information and is not suitable as an effective visual word

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