Method for recognizing minority clothing images
A technology of minority and recognition methods, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of improving recognition efficiency, clear tone levels, and reducing over-learning problems
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Embodiment 1
[0023] Embodiment 1: as Figure 1-6 As shown, a recognition method for minority clothing images, the specific steps are as follows:
[0024] Step1: Carry out human body detection on the image G to be recognized and the training image T, and use the k-poselet (k>1) deformable part model to detect each independent poselet, realize the overall and partial detection of the human body, and obtain the detected body parts to be recognized respectively Image G' and the detected training image T';
[0025] Step2 respectively extract five underlying features of the detected image to be recognized G' and the detected training image T' respectively color histogram, HOG, LBP, SIFT and edge operator, and obtain the image to be recognized after feature extraction G" and The training image T" after feature extraction;
[0026] Step3 Define the semantic attributes of ethnic minority clothing, mark the semantic attributes of the detected training image T', use the multi-task feature model to ...
Embodiment 2
[0031]Embodiment 2: In this embodiment, the image of ethnic minority clothing in Yunnan is taken as an example for illustration.
[0032] Step1, first input the minority clothing image G to be identified and input the training image T from the Yunnan minority clothing image library, use the weight vector ω=(M 0 ,...,M j ..., M k-1 , d 1 ,...,d j ... d k-1 ,b) describe each k-poselet, where, M j is the appearance template, d j is the spatial deformation model of the jth pose of k-poselet, b is the bias, and each k-poselet is described by a weight vector when detecting the model.
[0033] Then, k separate HOG templates are used to simulate the appearance model of each part, human detection is performed on each independent poselet, and keypoint prediction is performed from the average position of poselet positions and scales in the training data. Use average maximum precision (AMP) to measure whether a k-poselets set C achieves high precision and high coverage:
[0034] ...
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