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Image visual characteristic extraction method based on sparse coding

A technology of image vision and feature extraction, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problem of lack of effective fusion of three kinds of information

Active Publication Date: 2013-03-13
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is currently a lack of a technical means to effectively integrate the three types of information.

Method used

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  • Image visual characteristic extraction method based on sparse coding
  • Image visual characteristic extraction method based on sparse coding
  • Image visual characteristic extraction method based on sparse coding

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Embodiment Construction

[0042] The image visual feature extraction method based on sparse coding that the present invention proposes comprises the following steps:

[0043] (1) Assuming that there are N pictures in the picture set, extract the underlying features of the picture set, where the underlying feature set of the i-th picture is in is the kth of the i-th picture i underlying features, k i =1,2,..., for collection The number of elements in i=1, 2,..., N;

[0044] (2) Set a threshold for the frequency of labels in the picture set, delete the labels whose frequency in the picture set is lower than the set threshold, and generate a label vector w for all the labels of the i-th picture in the picture set i , i=1, 2,..., N;

[0045] (3) Generate an underlying feature similarity matrix W, the specific process is as follows:

[0046] (3-1) Calculate the Euclidean distance between any two underlying features in the underlying feature set as follows:

[0047] | ...

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Abstract

The invention relates to an image visual characteristic extraction method based on sparse coding and belongs to the technical field of digital image processing of computers. The method comprises the following steps of: extracting low-level characteristics of a picture set; removing labels with extremely low frequency, and generating a label vector; generating a matrix W similar to the low-level characteristic to serve as a basis of manifold constraint, and essentially combining low-level visual characteristics and high-level textual characteristics; establishing a target function; and minimizing the target function, so as to obtain an optimal matrix consisting of sparse coding of the low-level characteristics of the picture set. According to the method, by the adoption of the sparse coding, hidden type information of the low-level visual characteristics and the high-level textual characteristics of an image is well mined, and a model has high robustness; according to the method, a maximization pool method is adopted, and a unique image visual characteristic vector of each picture is obtained; and moreover, the visual characteristics of a final image are simple and effective.

Description

technical field [0001] The invention relates to an image visual feature extraction method based on sparse coding, which belongs to the technical field of computer digital image processing. Background technique [0002] Image visual features are a kind of coding for images in the field of computer vision to enable machines to learn and perceive images. Visual features are divided into global features and local features. Commonly used global features include color features, texture features, etc., the most commonly used The local feature of is a scale invariant feature transform (hereinafter referred to as SIFT feature). [0003] Sparse coding is a coding technique that uses a set of ultra-complete bases to express a vector as sparsely as possible. It has been widely used in various fields of machine learning such as compressed sensing, image restoration, and face recognition, and has achieved good results. Effect. The success of sparse coding in the field of image processin...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
Inventor 丁贵广周继乐
Owner TSINGHUA UNIV
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