Method and system for searching images by images based on deep learning
A deep learning and image technology, applied in the field of image search based on deep learning, can solve problems such as difficult control of high-level features, and achieve the effect of meeting the needs of fast analysis, fast search results, and reducing impact
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[0024] Example 1:
[0025] Embodiment 1 of the present invention provides a system for searching images based on deep learning, such as figure 1 As shown, the image input platform 10, the integrated access gateway 20, the intelligent management server 30, and the intelligent analysis server 40 are included. The image input platform 10, the integrated access gateway 20, the intelligent management server 30 and the intelligent analysis server 40 are connected in sequence, specific:
[0026] The image input platform 10 is used for image entry, image transmission, image storage and image preprocessing; the integrated access gateway 20 is used for statistical access of the image input platform to the intelligent management server; the intelligent management The server 30 is used to manage and analyze resources; the intelligent analysis server 40 is a functional entity for image search and is composed of multiple image analysis units, each of which can independently complete the analysi...
Example Embodiment
[0027] Example 2:
[0028] Embodiment 2 of the present invention provides a method for searching images based on deep learning, which is characterized in that it includes:
[0029] In step 201, the image category features are calculated, and the trained deep convolutional neural network is used to extract the classification features of the input image;
[0030] In step 202, the image self-encoding feature is calculated, and the trained deep learning automatic encoding algorithm is used to extract the encoding feature from the input image;
[0031] In step 203, hybrid feature encoding compression, combining the classification features and image self-encoding features, and encoding these features through a deep learning automatic encoding algorithm;
[0032] In step 204, the image similarity is calculated according to the features, and the output is sorted.
[0033] This embodiment uses a deep convolutional neural network to generate high-level features to help analyze image categories to...
Example Embodiment
[0046] Example 3:
[0047] Embodiment 3 of the present invention combines actual cases to provide specific implementation methods for the implementation of Embodiment 1 and Embodiment 2. Specifically, it includes the five parts of computing image category features, computing image self-encoding features, computing custom features, hybrid feature encoding compression, and computing image similarity and sorting output as described in Embodiment 2.
[0048] Part 1: Calculate image category features
[0049] The algorithm for calculating image category features uses deep convolutional neural networks, such as the ``ImageNet Classification with Deep Convolutional Neural Networks'' algorithm described in the article. The network is composed of 5 convolutional layers and 3 fully connected layers. The image passes through the convolutional layer and The fully connected layer finally obtains the method of high-level image features, which are mainly used for image classification.
[0050] Th...
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