Method for classifying images by performing pairwise-constraint-based online dictionary reweighting

A pairing and image technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as background, weather, illumination, attitude restrictions, etc., and achieve the effect of reducing the influence of intra-class differences and background changes

Inactive Publication Date: 2012-06-20
INST OF AUTOMATION CHINESE ACAD OF SCI
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[0004] Although image classification technology has been widely used in many fields, there are still many difficulties to be solved
Most of th

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  • Method for classifying images by performing pairwise-constraint-based online dictionary reweighting
  • Method for classifying images by performing pairwise-constraint-based online dictionary reweighting
  • Method for classifying images by performing pairwise-constraint-based online dictionary reweighting

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[0023] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0024] The main idea of ​​the present invention is: 1) the commonly used aggregation operation in image classification can be regarded as a weak weighting, and the present invention obtains stronger weighting through machine learning; 2) the present invention uses pairwise constraints to realize dictionary reweighting, which can Effectively encode the relationship between images, reduce the impact of intra-class differences and background changes; 3) batch training is expensive in terms of computing time and memory consumption, the present invention proposes an online learning algorithm that can use very small cost The same or even better results are obtained; 4) the present invention obtains an analytical solution through...

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Abstract

The invention discloses a method for classifying images by performing pairwise-constraint-based online dictionary reweighting. The method comprises the following steps of: performing bottom-layer feature extraction on all images of a training set to construct an initial visual dictionary; performing feature transform on extracted bottom-layer features by adopting sparse coding to obtain coded features; performing maximum aggregation on the coded features to obtain a feature expressed by a vector for the classification of a classifier; and performing online dictionary reweighting on the feature expressed by the vector by utilizing pairwise constraints, and transmitting the feature to the classifier for training and classification. By the pairwise constraints, a relationship between pairwise images can be effectively coded; by an online learning algorithm provided on the basis of a conservative-radical training strategy, training time is greatly shortened, and incremental updating can be realized; and the method is particularly applied to massive datasets.

Description

technical field [0001] The invention relates to the technical field of image classification in computer vision, in particular to a method for object classification based on bag-of-words model and online learning. Background technique [0002] With the rapid improvement of computer computing power, computer vision, artificial intelligence, machine perception and other fields are also developing rapidly. Image classification, as one of the basic problems in computer vision, has also been greatly developed. Image classification is the use of computers to intelligently analyze images to determine the category to which the image belongs. [0003] With the development of Internet technology and computer technology, image classification has been widely used in many fields. Content-based image retrieval can retrieve images based on image content, and quickly obtain images that are similar in appearance to the retrieved image, which is incomparable to text-based image retrieval tec...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 谭铁牛黄凯奇任伟强赵鑫
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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