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Image retrieval method based on deep feature consistent hashing algorithm

A hash algorithm and image retrieval technology, which is applied in the field of deep learning, can solve the problems that cannot reflect the image similarity ranking well, achieve retrieval accuracy and time advantages, improve the loss function, and improve retrieval accuracy.

Active Publication Date: 2021-11-02
OCEAN UNIV OF CHINA
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Problems solved by technology

For multi-label images, this method does not reflect the similarity ranking of images well

Method used

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  • Image retrieval method based on deep feature consistent hashing algorithm
  • Image retrieval method based on deep feature consistent hashing algorithm
  • Image retrieval method based on deep feature consistent hashing algorithm

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Experimental program
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Embodiment 1

[0059] A kind of image retrieval method based on depth feature consistent hashing algorithm, comprises the following steps (such as figure 1 shown):

[0060] S1: First, get the semantic similarity matrix according to the label of the image data (such as figure 1 Semanticsimilarity matrix (semantic similarity matrix) part);

[0061] Given n training set images I={I 1 , I 2 , … , I n}, the value of n is a positive integer; first, the similarity matrix is ​​calculated using the labels. The traditional calculation method is, if I i and I j have any same label, then s ij =1, otherwise s ij =0. Follow the method of the predecessors, use the percentage to calculate s; the formula is as follows:

[0062] (1)

[0063] Among them, li and lj represent the label vectors of images Ii and Ij; represents the inner product of images Ii and Ij; according to formula (1), images are divided into two categories: strong similarity and weak similarity. Strong similarity is divided ...

Embodiment 2

[0095]In order to verify the effectiveness of the method, experiments were carried out on the widely used datasets Flickr and Cifar-10, and compared with other state-of-the-art methods. Flickr is a dataset containing 25,000 images, each image has at least a label. Resize the image to 227×227, an image may contain multiple labels. Cifar-10 is a color image dataset that is closer to general objects. Cifar-10 is a small dataset compiled by Hinton's students Alexkrizhevsky and Ilyasutskever to identify cosmic objects. There are 10 categories: Airplane, Car, Bird, Cat, Deer, Dog, Frog, Horse, Boat and Truck. The size of each image is 32×32, and each category has 6000 images. There are 50000 training images and 10000 testing images in the dataset.

[0096] For Flickr, 4000 images are randomly selected as the training set and 1000 images as the test set. Set λ=0.1, because λ will lead to more discretization, and too small value of λ will reduce the impact of the quantization los...

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Abstract

The invention discloses an image retrieval method based on a deep feature consistent hash algorithm. The method includes: obtaining multi-label or single-label image data, including a training set and a test set; preprocessing the training set; using the preprocessed training set to optimize the neural network; inputting the training set into the optimized neural network Obtain the hash code; the Hamming distance between the calculated hash code and the hash code obtained from the test set is sorted according to the distance from small to large, and the first k retrieval results are output to complete the retrieval. It is verified that the model proposed by the present invention has better retrieval performance than other existing baseline methods. In the retrieval of single-label and multi-label image data sets, the present invention has obvious advantages in retrieval accuracy and time compared with existing common methods.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a single-label and multi-label image retrieval method based on a deep feature consistent hashing algorithm. Background technique [0002] With the rapid development of multimedia big data, the number of images has exploded, which requires fast and accurate retrieval methods. Exact nearest neighbor retrieval (KNN) takes a long time and is not suitable for large data retrieval, while approximate nearest neighbor retrieval (ANN) is more popular due to the consideration of time and efficiency. [0003] Supervised learning is a common technique for training neural networks and decision trees. Both techniques, neural network and decision tree, are highly dependent on the information given by the predetermined classification system. For neural network, the classification system uses this information to judge the network error, and then continuously adjusts the network...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/51G06F16/583G06K9/62G06N3/04G06N3/08
CPCG06F16/51G06F16/583G06N3/08G06N3/048G06N3/045G06F18/22G06F18/241
Inventor 曹媛刘峻玮陶小旖桂杰
Owner OCEAN UNIV OF CHINA
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