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Zero sample image classification method based on relative attribute random forest

A random forest and sample image technology, applied in the field of pattern recognition, can solve problems such as the influence of image classification accuracy, low model accuracy, and unreasonableness

Active Publication Date: 2016-11-09
中国科学院电子学研究所苏州研究院
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Problems solved by technology

However, this method has certain disadvantages: (1) It is unreasonable to assume that all known and unknown class images obey the Gaussian distribution; , so it will be affected by human subjective factors and the accuracy of the model is not high; (3) There is a large error in the maximum likelihood estimation method, which will also affect the accuracy of image classification

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  • Zero sample image classification method based on relative attribute random forest
  • Zero sample image classification method based on relative attribute random forest
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Embodiment Construction

[0062] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0063] Zero-shot image classification methods based on random forests of relative attributes, such as image 3 shown, including the following steps:

[0064] Step 1: If Figure 4 In (1), given the underlying features of the known class image and the category label set {x 1 ,x 2 ,...,x S ;y 1 ,y 2 ,...,y S}, the underlying feature set of unknown class images {z 1 ,z 2 ,...,z U}, the ordered attribute pair set {O 1 ,...,O M}, the set of similar attribute pairs of known class images {S 1 ,...,S M}, the number T of random trees an...

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Abstract

The invention provides a zero sample image classification method based on a relative attribute random forest. An attribute ranking score model is built for an image with an unknown class according to the relative relationship between image classes and image attributes, the attribute ranking score models for all images serve as training samples to train a random forest classifier, and finally, according to an attribute ranking score for a tested image and the random forest classifier obtained through training, the label of the tested image is predicted. The method of the invention can realize zero sample image classification and has the advantages of high classification recognition rate, strong model stability and the like.

Description

[0001] technology zero field [0002] The invention belongs to the zero field of pattern recognition, in particular to a zero-sample image classification method based on relative attribute random forest. Background technique [0003] 1.1 Zero-shot image classification [0004] Zero-shot image classification is one of the research hotspots in the field of pattern recognition at present. Different from traditional image classification problems, the images classified and recognized by zero-shot image classification in the test phase are not involved in the training of the classifier model. like figure 1 As shown, the marked images in the training phase cover the three categories of "Lion", "Athletic shoes" and "Polar bear" (that is, known categories), while the images in the testing phase appear in the category of "Stiletto" (that is, unknown categories ), since the "Stiletto" category did not participate in the training of the classifier, the classifier will not be able to pre...

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Application Information

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IPC IPC(8): G06K9/62G06K9/66
CPCG06V30/194G06F18/2415
Inventor 乔雪彭晨段贺刘久云胡岩峰刘振
Owner 中国科学院电子学研究所苏州研究院
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