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Image classification algorithm and system based on manifold learning

A manifold learning and classification algorithm technology, applied in computing, computer parts, character and pattern recognition, etc., can solve the problems of complex operation, low classification accuracy and large amount of calculation.

Pending Publication Date: 2019-01-29
HUBEI UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] It can be seen that in traditional machine learning, image features are extracted first, and then imported into classifiers SVM (Support Vector Machine), KNN (k Nearest Neighbor), Random Forest, etc. for classification processing. There are problems such as large amount of calculation, complex operation, and low classification accuracy. , are urgently needed

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  • Image classification algorithm and system based on manifold learning
  • Image classification algorithm and system based on manifold learning
  • Image classification algorithm and system based on manifold learning

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Embodiment Construction

[0095] Such as figure 1 As shown, the image classification method in the present invention can be divided into 5 steps, step 1 selects the sample set required for training and the sample set required for testing, step 2 extracts the SIFT features of all samples, and step 3 uses manifold learning To reduce the high-dimensional features of all samples, step 4 uses the SVM classifier to train the sample set, and step 5 uses the trained model to classify the test sample set. Specific steps are as follows:

[0096] Step 1: Select the sample set required for training and the sample set required for testing.

[0097] Step 2: Extract the image features of the two sample sets by using the SIFT algorithm, such as figure 2 shown.

[0098] Step 2a: Construct the scale space. First, the Gaussian pyramid is established by convolving the image with the Gaussian function. The scale space of the two-dimensional image in the Gaussian pyramid is defined as formula 1-1:

[0099] L i (x, y, ...

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Abstract

The invention discloses an image classification algorithm and a system based on manifold learning. The method includes S1, selecting a training sample set and a testing sample set; S2, extracting feature points of image of two sample sets by using a sift algorithm; S3, respectively reducing the dimension of the feature points in the two sample sets by using the local linear embedding or Labras feature mapping in the manifold learning method; S4, inputting that reduced dimension feature point of the training sample set into a support vector machine classifier for training; S5, classifying the test sample set by using the trained support vector machine classifier. The invention combines the SIFT feature extraction algorithm with the non-linear manifold learning dimension reduction algorithm,extracts the middle-level feature of the image, and then classifies and processes the image by using the SVM classifier, thereby effectively improving the computing speed and the classification accuracy.

Description

technical field [0001] The invention relates to the technical field of image classification, and is applicable to the fields of object classification, object recognition, object detection and the like. Background technique [0002] Image classification technology is a technology that marks different types of targets as corresponding types according to the differences in image features. The traditional image classification methods first use the histogram of oriented gradient (Histogram of Oriented Gradient, HOG), LBP (Local Binary Pattern, local binary pattern) and Haar and other algorithms to extract the features of the image, and then use the classifier to classify different images into corresponding kind. According to the different extracted features, the final classification effect is also very different; therefore, it is very important to choose a suitable feature extraction method that can fully describe the image and filter out more features that are beneficial to ima...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/213G06F18/2411G06F18/214
Inventor 王云艳罗冷坤王重阳
Owner HUBEI UNIV OF TECH
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