Probabilistic latent semantic model object image recognition method with fusion of significant characteristic of color

A technology of object images and semantic models, applied in the field of image recognition, can solve problems such as not fully considering the distribution of visual words

Active Publication Date: 2012-08-08
猫窝科技(天津)有限公司
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Problems solved by technology

However, the traditional BOW model only uses the information of visual words in a single image

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  • Probabilistic latent semantic model object image recognition method with fusion of significant characteristic of color
  • Probabilistic latent semantic model object image recognition method with fusion of significant characteristic of color
  • Probabilistic latent semantic model object image recognition method with fusion of significant characteristic of color

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Embodiment Construction

[0081] 1. In the robot training stage, because the robot recognition needs to build a training database first, the pre-collected training images will be defined according to the purpose of the object in the image. N categories, category numbers are 1 to N, and each image category contains T images , the entire training image set P train The total number of images in is: N×T=Q;

[0082] 2. For each image in the training image set, the SIFT algorithm is used to calculate the salient feature points of each training image and generate HSV_SIFT salient features. The main steps are as follows: image feature point detection, retain salient feature points, and determine the main direction of salient feature points , generate SIFT features of salient feature points, generate image color features, merge SIFT features of salient feature points and image color features, generate HSV_SIFT salient features, and finally construct training image set P train HSV_SIFT significant feature libra...

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Abstract

The invention provides a probabilistic latent semantic model object image recognition method with the fusion of a significant characteristic of a color, belonging to the field of image recognition technology. The method is characterized by: using an SIFT algorithm to extract a local significant characteristic of an image, adding a color characteristic simultaneously, generating a HSV_SIFT characteristic, introducing TF_IDF weight information to carry out characteristic reconstruction such that the local significant characteristic has discrimination more, using a latent semantic characteristicmodel to obtain an image latent semantic characteristic, and finally using a nearest neighbor KNN classifier to carry out classification. According to the method, not only is color information of theimage considered, but also the distribution of a visual word in a whole image set is fully considered, thus the local significant characteristic of an object has discrimination more, and the ability of recognition is raised.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and introduces an object image recognition method of a salient feature probability latent semantic model that fuses color information. Add color information while extracting the salient features of the image, and introduce the TF-IDF (term frequency-inverse document frequency) word frequency weight statistical method to make the local salient features more distinguishable. On this basis, the latent semantic features of the image are obtained according to the latent semantic model , to narrow the semantic gap existing in object recognition, and it is easier to solve the problem of image recognition. Background technique [0002] At present, mobile robots have been widely used in many fields such as industry, aerospace, military, and service. With the expansion of application fields, people have higher and higher requirements for the intelligence of mobile robots. Intelligent autonomous...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 杨金福王锴李明爱王阳丽杨宛露傅金融
Owner 猫窝科技(天津)有限公司
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