Image retrieval method based on decomposable word pack model

An image retrieval and model technology, applied in the field of computer vision and pattern recognition, can solve the problem that the bag of words model cannot adapt to the change of feature types, the bag of words model cannot cope with the change of feature types, and the effect of feature retrieval is not considered.

Active Publication Date: 2017-05-03
BEIHANG UNIV
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Problems solved by technology

For example, the method based on statistical ranking does not consider the retrieval effect of the feature itself, and only uses the simple median as the final retrieval result
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  • Image retrieval method based on decomposable word pack model
  • Image retrieval method based on decomposable word pack model
  • Image retrieval method based on decomposable word pack model

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Embodiment Construction

[0033] The present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. This application proposes an image retrieval method based on a decomposable word bag model, the method flow is as follows figure 1 As shown, it includes the following four parts: first, extract multiple features from the image, and build an index for each feature separately; second, for the index in the above features, use the bag of words model to organize each feature data, and create an index for each feature The feature data is clustered, and the cluster center is used as the entry in the bag of words model; third, a linear discriminant function is established for multi-feature retrieval, and the significance weight of the feature is learned by the minimum mean square error criterion through the pre-retrieval process, and used as a linear discriminant function. The coefficients in the discriminant function; Fourth, in the retriev...

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Abstract

The invention provides an image retrieval method based on a decomposable word pack model. The method comprises the following four portions: first, extracting a plurality of features of an image, and separately establishing an index for each feature; second, in view of the indexes in the features, organizing each feature data via the word pack model, clustering the feature data, and using a clustering center as an entry in the word pack model; third, establishing a linear judgment function for the retrieval of the plurality of features, learning significant weights of the features by the least-mean-square-error criterion in a pre-retrieval process, and using the significant weights as coefficients in the linear judgment function; and fourth, independently providing a candidate set of each feature in a retrieval process. The final retrieval results of the plurality of features are clustered by the linear judgment function by using the significant weights of the features. By adoption of the method, high retrieval accuracy can be obtained, and the method is suitable for actual large-scale image retrieval applications.

Description

technical field [0001] The present application relates to an image retrieval method, in particular to an image retrieval method based on a decomposable word bag model, belonging to the fields of computer vision and pattern recognition. Background technique [0002] With the development of multimedia technology, digital images are widely used in the Internet, satellite systems, information management and various monitoring systems because of their intuitive and vivid forms. Under the background of large-scale application of digital images, their number is increasing rapidly. Therefore, in the face of such a huge, real-time expanding, and constantly changing image database, how to effectively organize and manage it, and how to find the desired image in the vast image database has become a research hotspot in related fields. [0003] Aiming at the limitation that the traditional image retrieval based on this article is inefficient and unable to retrieve images at the image con...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/583
Inventor 姜帆胡海苗郑锦李波
Owner BEIHANG UNIV
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