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bottom-up-attention

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

computervision-localfeatures

Computer Vision - Local Features (HARRIS, MSER, SIFT, PCA-SIFT, GLOH)

cvcode

This is a code repository about CV.

environmentalsoundclassification

Here, an algorithm to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance is proposed. We use signal sub-band energy to construct signal-dependent dictionary and matching pursuit algorithms to obtain a sparse representation of a signal. The coefficients of the sparse vector are used as weights to compute weighted features. These features, along with mel frequency cepstral coefficients (MFCC), are used as feature vectors for classification. Experimental results show that the proposed method gives an accuracy as high as 95.6 %, while classifying 14 categories of environmental sound using a Gaussian mixture model (GMM). For more details, please refer to [1].

gcn_hsi_classification

It's a experiment that applying the graph convolution neural network for hyperspectral image classification

glaucomatous-image-classification-

A MATLAB program to classify glaucomatous fundus images using HOG (Histogram of Oriented Gradients) feature descriptor with SVM and Naive Bayes Classifier

mnistgnn

MNIST classification by using GCN

multi-scale-multi-task-network-model

object_classification

Computer vision project in matlab for building image feature space for object recognition and classification

orl_faces

ORL人脸识别不同算法的实现，用到了scikit-learn,tensorflow等，任选5张训练，5张测试。因为每次训练随机挑选，所以每次输出识别率有偏差 算法 识别率 bp神经网络 0.8 pca+bp神经网络 0.85 小波变换+pca+bp神经网络 0.95 CNN 0.98 小波变换+pca+SVM 0.98####同时希望大家提出宝贵意见，欢迎学习交流，如果你喜欢该项目，请star或者fork一下，你的主动将是我前行的动力####

pub-2017-compag-srdl-leaf

Joint SR and DL for Robust Classification

randomised-decision-forest

Categorise Caltech 101 images based on random forest.

scdf

Supervised Sparse Coding With Decision Forest-IEEE Signal Processing Letters 2019 By jointly conducting sparse coding and classifier training, supervised sparse coding has shown its effectiveness in a variety of recognition tasks. However, the existing supervised sparse coding methods often consider linear classification, which limits their discrimination in handling highly nonlinear data. In this letter, we propose a new supervised sparse coding model by incorporating decision tree classifiers. Since decision trees can well deal with the non-linear properties of data, the introduction of decision trees to sparse coding can noticeably improve the discrimination of coding. Meanwhile, sparse coding is able to produce sparse de-correlated features that decision tree is in favor of. For further improvement, we close the loop of sparse coding and decision tree learning with an ensemble framework, which alternatively learns a dictionary for sparse coding and a decision tree for classification. The resulting series of decision trees as well as series of dictionaries are used to construct a decision forest for classification. The proposed method was applied to face recognition and scene classification, and the experimental results have demonstrated its power in comparison with recent supervised sparse coding methods.

srcfs

MATLAB code for Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (SRCFS) (KBS 2019)

thesis_code

Code for my master's thesis whose name is "The Research on Discrimination Constraint based Collaborative Representation for Classification".

weighted-majority-vote-object-recognition

This project uses machine learning algorithms in matlab to predict image object categories on the Caltech 101 dataset.