A Correlation Feedback Method for Actively Selecting Multi-Instance and Multi-Labeled Digital Images

A digital image and related feedback technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as inability to adapt to automatic image labeling, limited information, etc.

Active Publication Date: 2019-05-14
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Abstract
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Problems solved by technology

The information obtained by this feedback method is very limited, and it cannot be adapted to the problem of automatic image annotation under multi-instance multi-label representation.

Method used

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  • A Correlation Feedback Method for Actively Selecting Multi-Instance and Multi-Labeled Digital Images
  • A Correlation Feedback Method for Actively Selecting Multi-Instance and Multi-Labeled Digital Images
  • A Correlation Feedback Method for Actively Selecting Multi-Instance and Multi-Labeled Digital Images

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Embodiment

[0056] Such as figure 1 Shown is the working flow diagram of the digital image automatic labeling device. Assume that the training image data set consists of two parts, one part has been marked, assuming a total of N 1 images, denoted by L; the other part is unmarked and fed back by users, assuming that there are N 2 image, denoted by U. The device extracts the images in the data set according to the characteristics of the multi-instance multi-label learning input. Each image is represented by a set of feature vectors, and each feature vector is called an example. Feature extraction can use the classic methods in machine learning textbooks to generate applicable image features, such as image segmentation first, and then extracting features such as color, texture, and shape for each image block. First, according to the feature vector of the image in L (assuming X L Represented) and the related information between the image and the mark (assuming Y L , when the value is 1, ...

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Abstract

The invention discloses a correlation feedback method for actively selecting multi-instance multi-label digital images. Multi-instance multi-label learning is a new machine learning framework proposed in recent years, which has been successfully applied in many practical problems. The automatic image annotation technology based on multi-instance multi-label input representation can be well applied to real-world tasks. However, with the enhancement of its expressive ability, the demand for marked training samples of automatic image annotation methods increases sharply as the representation space becomes larger. The present invention combines multi-instance multi-label learning and active learning technology in machine learning, without increasing the cost of user labeling, and obtains more detailed and rich label information in the process of each relevant feedback, thereby improving the system to a greater extent Labeling accuracy effectively reduces the user's participation cost.

Description

technical field [0001] The invention belongs to the technical field of digital image automatic labeling, and in particular relates to a correlation feedback method for actively selecting multi-instance and multi-label digital images. Background technique [0002] With the popularity of digital products and the popularity of various social networking sites, digital images have become the carrier of more and more Internet content. In order to efficiently utilize these digital image data, one of the core and most difficult tasks is to let the computer understand the semantics of the image, and automatic image annotation is the key technology. Current automatic image annotation techniques tend to represent images as single examples and only focus on the relevance of images to a single semantic category. However, images often have complex semantics and contain multiple object entities, so the representation of a single instance and a single label will cause information loss, and...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2155G06F18/2178
Inventor 黄圣君高能能
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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