92 results about "Local feature descriptor" patented technology

The invention provides a method for obtaining compact global feature descriptors of an image and an image retrieval method. The method for obtaining the compact global feature descriptors of the image comprises the following steps: obtaining at least a local feature descriptor of the image; selecting one or more local feature descriptors from all the local feature descriptors; carrying on dimensionality reduction to the selected local feature descriptors; obtaining the local feature descriptors after dimensionality reduction; converting the local feature descriptors after dimensionality reduction into global feature descriptors for expressing visual feature of the image according to a first rule; carrying on data compression to the global feature descriptors; and obtaining the compact global feature descriptors of the image, wherein byte size of the global feature descriptors can be varied according to the variation of parameter values in the first rule. Through the adoption of the method, the obtained global feature descriptors are more compact and have scalability, and the defect that a mobile terminal with lower internal memory has insufficient space in the prior art can be solved.

A method and system for classifying tissue endomicroscopy images are disclosed. Local feature descriptors are extracted from an endomicroscopy image. Each of the local feature descriptors is encoded using a learnt discriminative dictionary. The learnt discriminative dictionary includes class-specific sub-dictionaries and penalizes correlation between bases of sub-dictionaries associated with different classes. Tissue in the endomicroscopy image is classified using a trained machine learning based classifier based on the coded local feature descriptors encoded using a learnt discriminative dictionary.

The invention discloses a few-sample image classification method and system based on mutual neighbors, and the method comprises the steps: (1) carrying out forward derivation of the visual feature representation of a query image and a support image through a neural network model; (2) screening local feature descriptors related to tasks in the few-sample classification by using a mutual neighbor algorithm; (3) calculating the similarity between the query image and each category in the support image set by using the screened descriptors; (4) carrying out few-sample task division by using the image data set with the label and then training a neural network model; and (5) sorting according to the similarity between the classes in the support image set, and selecting the class with the maximum similarity as the class prediction of the image. Interference of a large number of local feature descriptors from a background on similarity calculation can be eliminated in a training process of few-sample image classification, so that a classification result is more robust.

Feature descriptor matching described herein may include receiving a first input image and a second input image. A feature detector may detect features from the first and second input images. A descriptor extractor may learn local feature descriptors from the features of the first and second input images based on a feature descriptor matching model trained using a ground truth data set. The descriptor extractor may determine a listwise mean average precision (mAP) rank of a pool of candidate image patches from the second input image with respect to a queried image patch from the first input image based on the feature descriptor matching model, the first set of local feature descriptors, and the second set of local feature descriptors. The descriptor matcher may generate a geometric transformation between the first input image and the second input image based on the listwise mAP and a convolutional neural network.