Scene recognition with bag of SIFT and Support Vector Machine (SVM)

Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).

计算机视觉课程项目3：分别组合两种图像表示方法（Tiny images representation, get bags of words）和图像分类方法（KNN, SVC）对场景进行识别和分类

•Hybrid Image, Image Pyramid, Edge Detection •Detection, Description, and Matching •Face Recognition using Eigenface and Fisherface methods •Scene Recognition with Bag of Words •Final Project: Deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in Pet Image Recognition

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This repository is a computer vision project. In this project, classification is done for set of images into pre-defined classes using SVM and KNN and Bag of Words Implementation.

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Video scene recognition with bag of words

Scene Recognition with Bag of Words and Tiny Image Representation

Tiny images representation, SIFT, HOG, nearest neighbor classifier, support vector machine (SVM)

This is the third mini project of Computer Vision course ( CIE 552 ) at UST - Zewail City. The objective is to classify a set of test images using Bag of Words technique and compare it with another basic method "Tiny images".

The goal of this project is to solve the classical computer vision topic, Scene Recognition with Bag of Words.

Performed classification of scenes by training and testing on the 15 scene database (Lazebniket al. 2006) using Bag of Words and Spatial Pyramid

Scene recognition using knn and SVM with bag-of-words representation.

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SCENE RECOGNITION WITH BAG OF WORDS

Scene Recognition with Bag of Words - Machine Learning for Computer Vision

Scene Recognition with Bag of Words Feature Extraction and Nearest Neighbor/SVM Classifiers

Scene Recognition with a bag of words using Machine learning and Computer vision.

Implementation of Epipolar Geometry for stereo vision and Scene Recognition using Bag of Visual Words (BoVW) with SIFT, K-Means, and SVM/KNN classifiers.

Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).

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Project 4 for SFU CMPT-762 Computer Vision

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Project 3: Scene recognition with bag of words

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