Deep learning-based image searching method and device
A deep learning and image technology, applied in the field of image search, can solve the problems that cannot meet the needs of modern Internet applications, low image search accuracy and speed, low search algorithm performance, etc., to achieve fast retrieval speed, high accuracy, The effect of meeting application needs
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Embodiment 1
[0058] Such as figure 1 As shown, a deep learning-based image search method is based on the deep learning ResNET50 network, using a large number of images to train the image feature extraction network model, and then loading the model to extract features from the images in the image database to realize image search The function of the same or similar pictures; the specific steps are as follows:
[0059] Step 1, ResNET50 network model construction: use the DL4J deep learning framework to build a ResNET50 network model.
[0060] Step 2, model training: use the ImageNet2012 dataset to train the ResNET50 network model.
[0061] Step 3, image index database design: the fields of the table include "id" is the serial number of the image, "locaPath" is the storage path address of the image on the server, "imgName" is the name of the image and "imgFeature" is the character string of the image feature vector. Table creation statement: CREATE TABLE `image_features` ( `id` int NOT NULL ...
Embodiment 2
[0071] A device for searching images by images based on deep learning includes a memory: used to store executable instructions; and a processor: used to execute the executable instructions stored in the memory to implement a method for searching images by images based on deep learning.
Embodiment 3
[0073] This embodiment is to realize some function codes of this scheme:
[0074] / **
[0075] * Calculate the image feature feature of the input image imgFile
[0076] *
[0077] * @param imgFile
[0078] * @return feature
[0079] *
[0080] * * /
[0081] public String extractImgFeature(File imgFile, ComputationGraphmodelPretrained) throws IOException {
[0082] / / list to store image features
[0083] list imgFeatures = new ArrayList();
[0084] / / Load the ResNet50 weight file trained by ImageNet
[0085] / / ComputationGraph modelPretained = ModelSerializer.restoreComputationGraph(new File(modelPath));
[0086] / / ComputationGraph modelPretrained =loadResNet50PretrainedModel();
[0087] / / System.out.println("model summary:\t" +modelPretained.summary());
[0088] / / load image
[0089] NativeImageLoader nativeImageLoader = new NativeImageLoader(224, 224,3);
[0090] INDArray imgIndarray = nativeImageLoader.asMatrix(imgFile);
[0091] / / model feed f...
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