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Improved latent Dirichlet allocation-based natural image classification method

A natural image and classification method technology, applied in the field of image processing, can solve the problems of reducing classification accuracy and shortening classification time, achieve the effect of complete feature information extraction and improve average classification accuracy

Inactive Publication Date: 2014-06-18
XIDIAN UNIV
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Problems solved by technology

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

Method used

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  • Improved latent Dirichlet allocation-based natural image classification method
  • Improved latent Dirichlet allocation-based natural image classification method
  • Improved latent Dirichlet allocation-based natural image classification method

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Embodiment Construction

[0019] Reference figure 1 The specific implementation steps of the present invention are as follows:

[0020] Step 1: Use the grid block method to perform grid dense sampling on each natural image, and obtain the corresponding grid sampling points of each natural image.

[0021] The dense grid sampling of each natural image is to divide each natural image evenly with horizontal and vertical lines to obtain each grid sampling point of each natural image.

[0022] Step 2. Use the Scale Invariant Feature Transformation (SIFT) algorithm for each grid sampling point to extract its Scale Invariant Feature Transformation (SIFT) features.

[0023] (2a) Use each grid sampling point in the natural image as a key point for generating SIFT features;

[0024] (2b) Sampling in an N×N neighborhood window centered on the key point, and using a histogram to count the magnitude of the gradient direction of the neighborhood pixels, N is an even number not less than 2;

[0025] Preferably, N=4;

[0026] (2c...

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Abstract

The invention discloses an improved latent Dirichlet allocation-based natural image classification method, and mainly aims to solve the problems that the existing entire-supervision natural image classification method has a long classification time and the classification accuracy is degraded on the premise of shortening the classification time. The improved latent Dirichlet allocation-based natural image classification method has the implementation steps: performing dense grid sampling on each natural image to get grid sampling points thereof; extracting SIFT (scale-invariant feature transform) features of each grid sampling point; performing K clustering on the SIFT features to generate a visual dictionary; performing quantification on the natural images into visual documents by virtue of the visual dictionary; constructing a two-layer space pyramid for each visual document to obtain five visual documents; inputting the five visual documents of each natural image into an LDA model to obtain five latent semantic theme distributions; connecting the latent semantic theme distributions of all the natural images in sequence and then inputting to an SVM classifier for classification, to obtain the classification result. Compared with the classical classification method, the improved latent Dirichlet allocation-based natural image classification method has the advantage that the classification accuracy is increased while the average classification time is shortened. The improved latent Dirichlet allocation-based natural image classification method can be used for target recognition.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and relates to a classification method for 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 huge challenges to image recognition, retrieval, and classification. How to accurately obtain and process the information required by users from the vast data has become one of the urgent problems in this field. The purpose of natural image classification is to classify images into different categories based on the content contained in the image 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 Csurka G and others from the idea of ​​the bag of words BoW model to the field of image processing, see Csurka G, Dance C, Fan L, et al. Visual Categor...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 焦李成刘芳韩冰马文萍王爽马晶晶侯彪白静
Owner XIDIAN UNIV
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