Image Hash code training model algorithm and classification learning method based on binary weight
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Embodiment 1
[0067] A hash code image training model algorithm based on binary weights, the algorithm includes the following steps:
[0068] Step 1.1. Select the exponential loss function as the loss function used, and the formula of the objective equation is:
[0069]
[0070]
[0071] Let the generated image binary code be b i is the original training dataset middle x i For the corresponding r-bit binary code, let a linear hash equation be:
[0072] b=sgn(P T x)
[0073] here P is the image hash transpose matrix; T is the transpose symbol; d is the dimension of image x; r is the hash code length;
[0074] The binary code of the classifier is w.
[0075] Step 1.2: Perform unified learning on the classifier obtained in step 1.1 and the binary code of the training image feature, update the hash code of the training image feature and the binary code of the classifier, optimize the objective equation of the loss function selected in step 1.1, and get optimized After the imag...
Embodiment 2
[0106] On the basis of Embodiment 1, different loss functions are selected, and the steps of a hash code image training model algorithm based on binary weights are as follows:
[0107] Step 1.1. Select a simple linear loss function as the loss function used, and the formula of the objective equation is:
[0108]
[0109]
[0110] Let the generated image binary code be b i is the original training dataset middle x i For the corresponding r-bit binary code, let a linear hash equation be:
[0111] b=sgn(P T x)
[0112] here P is the image hash transpose matrix; T is the transpose symbol; d is the dimension of image x; r is the hash code length;
[0113] The binary code of the classifier is w.
[0114] Step 1.2: Perform unified learning on the classifier obtained in step 1.1 and the binary code of the training image feature, update the hash code of the training image feature and the binary code of the classifier, optimize the objective equation of the loss functio...
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