Image visual attribute excavation method 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: 2016-06-29
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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  • Image visual attribute excavation method based on sparse factor analysis
  • Image visual attribute excavation method based on sparse factor analysis
  • Image visual attribute excavation method 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 in conjunction with 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 mined from 435 face images in the Caltech101 dataset, the first step is to extract the gradient histogram features for each image (the feature extraction parameters are: the image is decomposed into 8 pixels × 8 pixels of cells, in each The gradient direction histogram of 0°-180° is counted in the cell, and then normalized by 2 cell × 2 cell blocks, a descriptor (or feature vector) is generated for each cell, and finally all the cells are concatenated ...

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Abstract

The invention provides an image visual attribute excavation method based on sparse factor analysis. The technical scheme comprises following steps of firstly calculating a characteristic matrix of an image set: calculating a histogram characteristic vector in a D-dimensional gradient direction of each of images Ii in the image set I so as to obtain the characteristic matrix of the image set; and secondly: excavating visual attributes of the image set: initializing a visual attribute matrix A into the front K line of X, initialing a visual attribute mixing coefficient matrix Y into a unit diagonal matrix, and carrying out iteration via alterative optimization so as to obtain the optimal visual attribute matrix A. According to the invention, image visual attributes are automatically excavated, so a disadvantage of demanding a large number of manual marking samples of attribute learning is overcome; and by intruding sparse constraints during optimization of the visual attribute mixing coefficient matrix, distinguishing performance of the visual attributes is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of image analysis, in particular to the technical field of semantic description in image analysis, and more specifically, to an image visual attribute mining method based on sparse factor analysis. Background technique [0002] Image visual attributes (visualattribute) enhance the consistent understanding of human and computer image content. 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, and there are two problems: one is that the manual labeling of images is inefficient and not suitable for large-scale image collections; the other is that Different people hav...

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Application Information

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Patent Type & Authority Applications(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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