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Salient object detection method based on multi-level context information fusion

An object detection and context technology, applied in the field of image processing, can solve the problem of not making full use of context information, and achieve the effect of accurate salient object detection

Active Publication Date: 2021-04-30
NANKAI UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

[0005] The purpose of the present invention is to solve the technical problem that the context information contained in the image cannot be fully utilized in the prior art, and to provide a salient object detection method based on multi-level context information fusion

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  • Salient object detection method based on multi-level context information fusion
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  • Salient object detection method based on multi-level context information fusion

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Embodiment Construction

[0018] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0019] A salient object detection method based on multi-level context information fusion, the specific operation of this method is as follows:

[0020] a. This network model is an "encoding-decoding" convolutional neural network model with mirror connections, and the encoding part can be mentioned in the article "Very Deep Convolutional Networks for Large-Scale Image Recognition" published by Karen Simonyan The VGG16 architecture can also be the ResNet architecture mentioned in the article "Deep residual learning for image recognition" published by Kaiming He, or other basic network architectures. For the VGG16 network, such as figure 1 As shown, in the basic network architecture, we fi...

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Abstract

A method for salient object detection based on the fusion of multi-level context information. The purpose of this method is to construct and utilize multi-level contextual features for image saliency detection. This method designs a new convolutional neural network architecture. This new convolutional neural network architecture is optimized from high-level convolution to bottom-level convolution, so as to extract context information on different scales for images. The fusion of contextual information can obtain high-quality image saliency maps. The salient regions detected by this method can be used to assist other visual tasks.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a salient object detection method based on multi-level context feature fusion. Background technique [0002] Salient object detection, also known as saliency detection, aims to simulate the human visual system to detect salient objects or regions in an image. Salient object detection techniques have broad applications in computer vision, such as image retrieval, visual tracking, scene classification, content-based video compression, and weakly supervised learning. Although many important saliency models have been proposed, the accuracy of saliency detection is still unsatisfactory, especially in many complex scenes. [0003] Traditional saliency detection methods usually manually design many underlying features and prior knowledge, but these features and prior knowledge are difficult to describe semantic objects and scenes. Recent advances in salient object...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/32G06N3/04G06N3/08
Inventor 程明明刘云
Owner NANKAI UNIV
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