Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A technology of image retrieval and feature fusion, applied in still image data retrieval, image coding, image data processing, etc., can solve the problem of lack of local information in image representation, achieve the effect of reducing storage cost, saving storage cost, and strong discrimination
Pending Publication Date: 2021-01-01
HUAZHONG UNIV OF SCI & TECH
View PDF0 Cites 4 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
Therefore, when only fully connected layer features are used, the resulting image representation lacks local information
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0058] An image retrieval method based on feature fusion, such as figure 1 shown, including:
[0059] Model training step: establish a convolutional neural network for extracting image features, and use the training image set to train it to obtain a feature extraction network;
[0060] Multi-layer semantic floating-point descriptor construction step: extract at least one high-level semantic feature and at least one low-level image feature of the image, and fuse the extracted high-level semantic feature and low-level image feature to obtain the multi-layer semantic floating point of the image Descriptor; figure 1 and figure 2 As shown, in this embodiment, the high-level semantic features include a global descriptor (Global), an object descriptor (Object) and a salient region descriptor (Salient), and the underlying image features include a SIFT descriptor, wherein the global descriptor describes The global feature of the image, the object descriptor describes the object inf...
Embodiment 2
[0128] An image retrieval method based on feature fusion, this embodiment is similar to the above-mentioned embodiment 1, the difference is that in this embodiment, the high-level semantic features include global descriptors and object descriptors, and the underlying image features include SIFT descriptors .
[0129] For the specific implementation of this embodiment, reference may be made to the description in Embodiment 1 above, which will not be repeated here.
Embodiment 3
[0131] An image retrieval method based on feature fusion, this embodiment is similar to the above-mentioned embodiment 1, the difference is that in this embodiment, high-level semantic features include global descriptors, and low-level image features include SIFT descriptors.
[0132] For the specific implementation of this embodiment, reference may be made to the description in Embodiment 1 above, which will not be repeated here.
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
PUM
Login to view more
Abstract
The invention discloses an image retrieval method based on feature fusion, which belongs to the field of image retrieval and comprises the following steps of: training a feature extraction network; extracting a multi-layer semantic floating point descriptor of each image in a training image set, and performing hash learning to generate a rotation matrix R; extracting a multi-layer semantic floating point descriptor of each image in an image library, and performing binaryzation after rotation by utilizing the R; classifying the images in the image library by using a classification network; correspondingly storing a binary descriptor and a class probability vector of each image, wherein the extraction of the multi-layer semantic floating point descriptor is implemented by extracting high-layer semantic features and bottom-layer image features of each image and fusing the features, the high-layer semantic features comprise global descriptors, and are extracted in a mode of zooming each image to a plurality of different scales, extracting features by using the feature extraction network and fusing the features, the bottom-layer image features comprise SIFT descriptors, and are extracted in a mode of extracting a plurality of SIFT features of each image and aggregating the SIFT features into VALD. According to the image retrieval method, the descriptor with high distinguishing capability and small occupied space can be constructed.
Description
technical field [0001] The invention belongs to the field of image retrieval, and more specifically relates to an image retrieval method based on feature fusion. Background technique [0002] The content-based image retrieval method extracts the visual features of the image to describe the image, which can describe the image more accurately and comprehensively than text. Scale Invariant Feature Transform (SIFT) was proposed by David in 1999 for image matching in the field of computer vision. SIFT not only has strong invariance to scale, translation and rotation, but also has good robustness to illumination changes, occlusion and noise. RootSIFT is an improved version of SIFT. RootSIFT performs 1-norm standardization and square root transformation on the basis of SIFT, thereby improving the descriptive power of SIFT. The vector of locally aggregated descriptors (Vector of Locally Aggregated Descriptors, VLAD) is used to encode the feature set into a fixed-length vector. VLA...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to view more 
Login to view more