A Mining Method of Image Visual Attributes Based on Sparse Factor Analysis

A technology of image vision and sparse factor, applied in the field of image analysis, it can solve the problems of semantic deviation and low efficiency of manual labeling process, and achieve the effect of improving the distinguishing performance.

Active Publication Date: 2019-01-15
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is: to overcome the low efficiency of the manual marking process and the semantic deviation introduced in the marking process in the existing image visual attribute learning method, based on the idea of ​​data-driven, adopt the unsupervised optimization method to directly learn from the large-scale image Mining both semantic and discriminative visual properties of images in collections

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  • A Mining Method of Image Visual Attributes Based on Sparse Factor Analysis
  • A Mining Method of Image Visual Attributes Based on Sparse Factor Analysis
  • A Mining Method of Image Visual Attributes Based on Sparse Factor Analysis

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Embodiment Construction

[0023] The image visual attribute mining method based on sparse factor analysis provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0024] figure 1 It is a schematic flow chart of the present invention. As shown in the figure, it includes two steps: the first step is to calculate the feature matrix of the image collection, and the second step is to mine the visual attributes of the image collection.

[0025] figure 2 In order to use the visual attribute results of the 435 face images in the Caltech101 dataset, the first step is to extract the gradient histogram feature for each image (the feature extraction parameters are: the image is decomposed into 8 pixel × 8 pixel cells, and in each image The gradient direction histogram of 0°-180° is counted in the cell, and then normalized with a 2-cell × 2-cell block to generate a descriptor (or feature vector) for each cell, and finally all cells are concatenated In th...

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Abstract

The invention provides an image visual attribute mining method based on sparse factor analysis. The technical solution includes the following steps: the first step is to calculate the feature matrix of the image collection. Calculate a D-dimensional gradient orientation histogram feature vector for each image Ii in the image set I, and obtain the feature matrix X of the image set. In the second step, the visual properties of the image collection are mined. Initialize the visual attribute matrix A as the first K columns of X, initialize the visual attribute mixing coefficient matrix Y as a unit diagonal matrix, and use alternate optimization to iterate to obtain the optimal visual attribute matrix A. The invention automatically mines image visual attributes, avoids the disadvantage that a large number of manually marked samples are required for attribute learning, and greatly improves the distinguishing performance of visual attributes by introducing sparse constraints when optimizing the visual attribute mixing coefficient matrix.

Description

technical field [0001] The invention relates to the technical field of image analysis, in particular to the technical field of semantic description in image analysis, and more particularly, to an image visual attribute mining method based on sparse factor analysis. Background technique [0002] The visual attributes of images enhance the consistent understanding of image content between humans and computers. Semantic description methods based on image visual attributes have achieved great success in many fields such as image object recognition, image retrieval and image content labeling. However, the existing image visual attribute learning methods define visual attributes through the natural language description attached to the image, which requires a large number of manually labeled images. There are two problems: first, the efficiency of manually labeling images is low, and it is not suitable for large-scale image collections; second, Different people have different unde...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V20/10
Inventor 邹焕新孙浩周石琳计科峰雷琳李智勇
Owner NAT UNIV OF DEFENSE TECH
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