Fine grain image classification method based on common dictionary pair and class-specific dictionary pair

A classification method and fine-grained technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problem of weak image difference and achieve the effect of reducing computational complexity

Inactive Publication Date: 2017-05-31
TIANJIN UNIV
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Problems solved by technology

Fine-grained image classification has weaker image-to-image variability, making it more challenging to classify fine-grained images

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  • Fine grain image classification method based on common dictionary pair and class-specific dictionary pair
  • Fine grain image classification method based on common dictionary pair and class-specific dictionary pair
  • Fine grain image classification method based on common dictionary pair and class-specific dictionary pair

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[0046]Traditional image classification methods are not effective in fine-grained image classification problems. The main reason is that the class differences of fine-grained images are small, and the feature resolution of traditional image classification methods is not enough; secondly, the images of each subclass are semantically similar , often have common structural features to be excavated. In order to solve the above problems, this paper proposes the following solutions: First, the idea of ​​dividing the dictionary into two parts for learning: the public dictionary is composed of public dictionary atoms and the class-dependent dictionary is composed of class-dependent dictionary atoms. The public dictionary and the class-dependent dictionary are respectively used It is used to learn the common features and class-dependent features between various types of images, which makes the learned dictionary more discriminative; the second is to use the dictionary pair learning model...

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Abstract

The invention belongs to the digit image processing field, provides dictionary with better discrimination property, prevents solving problems of standard sparse coding, greatly shortens classification time, and allows coefficient to have certain discrimination property; the invention provides the fine grain image classification method based on common dictionary pair and class-specific dictionary pair; the method comprises the following steps: 1, extracting a SIFT characteristic matrix of image database training samples, and using a K-singular value decomposition method K-SVD to obtain an initialization dictionary; 2, building a dictionary learning model based on the common dictionary pair and class-specific dictionary pair; 3, using an iteration method to solve the dictionary model built in step 2, thus obtaining an integral dictionary D and a parse dictionary P, and using the parse dictionary to solve the tested sample sparse expression matrix; using a minimizing reconstruction error method to determine the image belonging class. The method is mainly applied to the digit image processing occasions.

Description

technical field [0001] The invention belongs to the field of digital image processing, and in particular relates to a fine-grained image classification method based on public dictionary pairs and class-dependent dictionary pairs. Background technique [0002] Traditional image classification mainly refers to coarse-grained image classification, which is to classify different types of images with relatively large semantic differences. For example, there are 102 different types of images in the Caltech-101 image library, including man-made tools, animals, plants, etc. The differences between these images are more obvious, so the classification is easier. figure 1 (a) shows a comparison of a pigeon and a hippocampus. [0003] In recent years, the significance of fine-grained image classification in computer vision research has become more and more obvious. Compared with coarse-grained image classification, the image library it uses often contains a variety of semantically simi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/24
Inventor 郭继昌张帆王楠
Owner TIANJIN UNIV
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