Natural Image Classification Method Based on Latent Dirichlet Allocation

Abstract

The invention discloses a natural image classification method based on potential Dirichlet distribution. The natural image classification method mainly solves the problems that an existing full supervision natural image classification method is long in classification time and reduces the classification accuracy on the premise that the classification time is shortened. The natural image classification method includes the implementation steps of obtaining hue, saturation, luminance and distinguishing characteristic images of each natural image, respectively conducting gridding dense sampling on the characteristic images to obtain gridding sampling points of the characteristic images, extracting SIFT characteristics in the peripheral region of each gridding sampling point, conducting K clustering on the SIFT characteristics of the characteristic images in the same kind to generate a vision dictionary, using the vision dictionary to quantize all the characteristic images into vision documents, sequentially connecting the vision documents, inputting the sequentially-connected vision documents into an LDA model to obtain potential semantic theme distribution, and inputting the potential semantic theme distribution of all the natural images into an SVM classifier to carry out classification so as to obtain classification results. Compared with a classic classification method, the natural image classification method shortens the average classification time, meanwhile, improves the classification accuracy and can be used for object identification.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a method for classifying natural images, which can be used for target recognition. Background technique [0002] In recent years, the massive increase in the number of images has brought great challenges to image recognition, retrieval, and classification. How to accurately obtain and process the information required by users from the vast amount of data has become one of the urgent problems to be solved in this field. The purpose of natural image classification is to divide images into different categories according to the content contained in the images for subsequent processing or management. Classic natural image classification methods include: [0003] Natural Image Classification Method Based on Bag-of-Words Model. This method is transplanted by CsurkaG et al. to the field of image processing, see CsurkaG, DanceC, FanL, etal.VisualCategorizationwithBagsofKeypoints....

AI Technical Summary

Problems solved by technology

Compared with the bag of words model method, this method greatly shortens the classification time, but reduces the classification accuracy.

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