Search Results for "matlab code for image classification using svm" - Page 2

EasyPR is an open-source license plate recognition system designed to detect and recognize vehicle license plates from images using computer vision and machine learning techniques. The project focuses primarily on recognizing Chinese license plates but also demonstrates general approaches to automatic number plate recognition systems. Built on top of the OpenCV computer vision library, EasyPR provides algorithms for detecting license plate regions in images, segmenting characters, and...

Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a meta-optimizer model that learns by observing and adapting to the optimization trajectories of other models. The project allows users to compare performance between traditional optimizers and the learned optimizer (L2L) on various benchmarks, demonstrating how optimization strategies can be learned through experience. ...

This repository provides baseline implementations of deep supervised hashing methods for image retrieval tasks using PyTorch. It includes clean, minimal code for several hashing algorithms designed to map images into compact binary codes while preserving similarity in feature space, enabling fast and scalable retrieval from large image datasets.

MATLAB code to identify specific colors of your choice in an image, using MATLAB. Visit letsrobo.wordpress.com for detailed working of the code.

NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets.

...Each of these algorithms has its peculiar data format; the specific format and how to reconstruct the entire dataset are illustrated in other sections below. Out of all the methods, SVM using the Libsvm [1] produced the most accurate and optimized result for its classification accuracy for the 20 classes. All the algorithm implementation was written Matlab. Download the code and Report here.

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and...

We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classification problems with millions of...

Code for Semi-Supervised Machine Learning Techniques, Self-Learning and Co-training used in the paper: Rania Ibrahim, Noha A. Yousri, Mohamed A. Ismail and Nagwa M, El-Makky. “miRNA and Gene Expression based Cancer Classification using Self-Learning and Co-Training Approaches”. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 495-498, 2013. --------------------------------------------------------------------------- For Self-Learning: java -jar -Xms1700m SelfLearner.jar [trainFile] [testFile] [labelFile] [unlabeledFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFile] For Co-Training: java -jar -Xms2500m CoTraining.jar [trainFile-Side1] [testFile-Side1] [labelFile-Side1] [unlabeledFile-Side1] [trainFile-Side2] [testFile-Side2] [labelFile-Side2] [unlabeledFile-Side2] [MappingFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFileSide1] [ClassifierModelFileSide2]

The VOC-DPM repository is an implementation of an object detection system built on deformable part models (DPMs) and latent SVM learning, specifically packaged as “voc-release5.” It is the companion code for Ross Girshick’s dissertation, and extends earlier work on discriminatively trained DPMs. The system supports a grammar-based representation for object models, allowing structures such as mixtures and hierarchies to represent parts and whole objects. It implements both latent SVM training...

OSU SVM is a Support Vector Machine (SVM) toolbox for the MATLAB numerical environment. The toolbox is used to create models for regression and classification using support vector machines.

libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. ...