Image Retrieval Method Based on Local Preserving Iterative Quantized Hashing
What is AI technical title?
AI technical title is built by PatSnap AI team. It summarizes the technical point description of the patent document.
An image retrieval and iterative technology, applied in the field of image processing, can solve the problems of reduced retrieval performance, increased storage space occupancy, and increased algorithm time complexity, etc., to achieve the effects of improving retrieval accuracy, reducing occupancy, and improving performance
What is AI technical title?
AI technical title is built by PatSnap AI team. It summarizes the technical point description of the patent document.
An image retrieval and iterative technology, applied in the field of image processing, can solve the problems of reduced retrieval performance, increased storage space occupancy, and increased algorithm time complexity, etc., to achieve the effects of improving retrieval accuracy, reducing occupancy, and improving performance
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 Construction
[0028] specific implementation plan
[0029] The specific implementation method and technical effects of the present invention will be further described below in conjunction with the accompanying drawings.
[0030] refer to figure 1 , the present invention realizes steps as follows:
[0031] Step 1, get the original image.
[0032] Extract 5000 images from a given image database MNIST or CIFAR-10 as raw images.
[0033] Step 2, perform feature extraction on the original image to obtain image feature data.
[0034] (2a) average the pixel values ββof the 3 color channels of each original image to obtain the grayscale image of the original image data;
[0035] (2b) Use the Gabor filter to filter the grayscale image in 4 scales and 8 directions to obtain 32 feature maps of the grayscale image;
[0036] (2c) Divide each feature map into sub-grids with a size of 4Γ4, take the mean value of all pixels in each sub-grid, arrange the mean values ββin a vector, and obtain the featur...
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 local locality preserving iterative quantization hash and mainly solves the problems that large-scale image retrieval has high memory usage rate and low retrieval performance. The method includes the steps of 1, extracting and normalizing original image features; 2, subjecting normalization data to principal components analysis to obtain a low-dimensional normalization data matrix; 3, dividing low-dimensional normalization data into training data and testing data; 4, construction a neighbor graph of image training data matrixes to obtain neighborhood relation of the image training data; 5, with the neighborhood relation as a constraint, updating a rotation matrix by iterative quantization; 6, acquiring a hash code of the image training data and that of the image testing data according to the rotation matrix; and 7, acquiring retrieval results according to a Hamming distance between the hash code of the training data and that of the testing data. The method has the advantages that memory consumption is lowered, image retrieval performance is improved, and the method is applicable to image retrieval services for mobile equipment, the internet of things and E-commerce.
Description
technical field [0001] The invention belongs to the field of image processing, and further relates to a fast retrieval method for large-scale image data, which can be used for image search services of mobile devices, the Internet of Things, e-commerce, and the like. Background technique [0002] In recent years, with the rapid development of the Internet, cloud computing, mobile devices, and the Internet of Things, the amount of global data has entered the ZB era and is still growing exponentially every year. Image data in big data is the same as human visual cognition, and plays an important role in information expression. How to efficiently retrieve valuable images to make full use of big data to gain benefits has become a major problem today. In order to efficiently retrieve valuable images in big data, people propose hashing algorithms. This algorithm can convert an image into a binary code sequence of a certain length. Since the binary code sequence can be directly st...
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