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Learning method for joint low-rank representation and sparse regression

A sparse regression, low-rank representation technology, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of improving accuracy

Active Publication Date: 2018-01-16
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] Using the multi-vision adaptive regression algorithm to solve the problem of automatically predicting the memorability of images, and obtain the relationship between image features and image memory under the optimal parameters;

Method used

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  • Learning method for joint low-rank representation and sparse regression
  • Learning method for joint low-rank representation and sparse regression
  • Learning method for joint low-rank representation and sparse regression

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Experimental program
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Embodiment 1

[0027] To study the characteristics of images and predict the degree of image memory, the embodiment of the present invention proposes a learning method that combines low-rank representation and sparse regression, see figure 1 , the method includes the following steps:

[0028] 101: Obtain an image memory data set;

[0029] Among them, the image memorability dataset [1] Contains datasets from SUN [11] 2,222 images of . The memory score of the image is obtained through Amazon Mechanical Turk's Visual Memory Game, and the image memory is a continuous value from 0 to 1. The higher the value, the harder the image is to remember. Sample images with various memory scores such as figure 2 shown.

[0030] 102: Perform feature extraction on the SUN data set with the image memory score label;

[0031] Among them, the extracted features include: SIFT (scale-invariant feature transform, Scale-invariant featuretransform, SIFT), Gist (search tree, Generalized Search Trees), HOG (dir...

Embodiment 2

[0037] The scheme in embodiment 1 is further introduced below in combination with specific calculation formulas, see the following description for details:

[0038] 201: Image Memorability Dataset [1] Contains datasets from SUN [17] 2,222 images of ;

[0039] Wherein, the data set is well known to those skilled in the art, and will not be described in detail in this embodiment of the present invention.

[0040] 202: Perform feature extraction on the pictures of the SUN dataset with the image memory score label, and the extracted SIFT, Gist, HOG and SSIM features constitute a feature library.

[0041] This database includes 2222 pictures in various environments, each picture is marked with the image memory score, attached figure 2 Shows a sample of images from the database labeled with memory scores. Features expressed as D. i Represents the dimensionality of such features, and N represents the number of images contained in the database (2222). These features constitut...

Embodiment 3

[0096] Combined with the specific experimental data, Figure 3 to Figure 4 The scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0097] The Image Memorability dataset contains 2,222 images from the SUN dataset. The memory score of the image is obtained through Amazon Mechanical Turk's Visual Memory Game, and the image memory is a continuous value from 0 to 1. The higher the value, the harder the image is to remember, sample images with various memory scores such as figure 2 shown.

[0098] This method adopts two evaluation methods:

[0099] Ranking Correlation Evaluation Method (Ranking Correlation, RC): Get the ranking relationship between the real memory ranking and the predicted memory score, and use the ranking-related Spearman correlation coefficient standard to measure the correlation coefficient between the two rankings. Its value range is [-1,1], and the higher the value, the closer the two sorts are...

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Abstract

The invention discloses a learning method for joint low-rank representation and sparse regression. The method comprises the following steps: extracting the features of a SUN data set with an image memory score label; putting low-rank representation and a sparse regression model under the same framework to form a whole, and constructing a joint low-rank representation and sparse regression model; and solving the automatic image prediction memorability problem using a multi-vision adaptive regression algorithm, getting the relationship between image features and image memory under optimal parameters, getting a relationship result under optimal parameters, predicting the image memory of a test set in a database, and verifying the prediction result based on relevant evaluation criteria. The memorability of an image area can be accurately predicted through a low-rank learning framework of joint low-rank representation and sparse regression.

Description

technical field [0001] The invention relates to the fields of low-rank representation and sparse regression, and is used for memory degree prediction of images, in particular to a learning method for joint low-rank representation and sparse regression. Background technique [0002] Humans have the ability to remember thousands of images, however not all images are stored in the brain in the same way. Some representative pictures are remembered at a glance, while other images are easily lost from memory. Image memory is used to measure how well images are remembered or forgotten after a specific period of time. Previous work has shown that memory for pictures is related to intrinsic properties of pictures, namely that memory for pictures is consistent across time intervals and across observers. In this context, just like studying many other high-level image attributes such as popularity, interest, emotion, and aesthetics, several research works started to explore the potent...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 刘安安史英迪苏育挺
Owner TIANJIN UNIV
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