state-of-the-art implementations related to visual recognition and search
http://rogerioferis.com/VisualRecognitionAndSearch2014/Resources.html
Source Code
Non-exhaustive list of state-of-the-art implementations related to visual recognition and search. There is no warranty for the source code links below – use them at your own risk!
Feature Detection and Description
General Libraries:
- VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris
Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. Seea=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxlY2N2MTJmZWF0dXJlc3xneDo3ZDllMzVhMDA4YzEzNmU2" style="color:rgb(165,88,88)">Modern
– Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on
features: Software
session training - OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)
Fast Keypoint Detectors for Real-time Applications:
- FAST – High-speed corner detector implementation for a wide variety of platforms
- AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV
2010).
Binary Descriptors for Real-Time Applications:
- BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
- ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations,
but not scale) - BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
- FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)
SIFT and SURF Implementations:
- SIFT: VLFeat, OpenCV, Original
code by David Lowe, GPU implementation, OpenSIFT - SURF: Herbert Bay’s code, OpenCV, GPU-SURF
Other Local Feature Detectors and Descriptors:
- VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.
- LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).
- Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and
rendering style (CVPR 2012).
Global Image Descriptors:
- GIST – Matlab code for the GIST descriptor
- CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)
Feature Coding and Pooling
- VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including
Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding. - Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)
Convolutional Nets and Deep Learning
- Caffe – Fast C++ implementation of deep convolutional networks (GPU / CPU / ImageNet 2013 demonstration).
id=software:overfeat:start" style="color:rgb(165,88,88)">OverFeat
– C++ library for integrated classification and localization of objects.- EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on
convolutional neural networks. - Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural
networks. - Deep Learning - Various links for deep learning software.
Facial Feature Detection and Tracking
- IntraFace – Very accurate detection and tracking of facial features (C++/Matlab API).
- Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection
task) - Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.
- Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable
part-based detectors but with significant acceleration (ECCV 2012). - Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).
- Poselets – C++ and Matlab versions for object detection based on poselets.
- Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark
localization (CVPR 2012).
Attributes and Semantic Features
- Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).
- Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank
- Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors
(ECCV 2010, NIPS 2011, CVPR 2012).
Large-Scale Learning
- Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
- LIBLINEAR – Library for large-scale linear SVM classification.
- VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.
Fast Indexing and Image Retrieval
- FLANN – Library for performing fast approximate nearest neighbor.
- Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
- ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing
(CVPR 2011). - INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).
Object Detection
- See Part-based Models and Convolutional
Nets above. - Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
- Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links
for state-of-the-art implementations. - OpenCV – Enhanced implementation of Viola&Jones real-time object
detector, with trained models for face detection. - Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR
2008).
3D Recognition
- Point-Cloud Library – Library for 3D image and point cloud processing.
Action Recognition
- ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).
- STIP Features – software for computing space-time interest point descriptors
- Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
- Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints
(ICCV 2009)
Datasets
Attributes
- Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
- aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
- FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
- PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
- LFW – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
- Human Attributes – 8,000 people with annotated attributes. Check also this link for
another dataset of human attributes. - SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
- ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
- Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for
the WhittleSearch data. - Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.
Fine-grained Visual Categorization
- Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.
- Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
- Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is
included. - Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
- Oxford Flower Dataset – Hundreds of flower categories.
Face Detection
- FDDB – UMass face detection dataset and benchmark (5,000+ faces)
- CMU/MIT – Classical face detection dataset.
Face Recognition
- Face Recognition Homepage – Large collection of face recognition datasets.
- LFW – UMass unconstrained face recognition dataset (13,000+ face images).
- NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
- CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
- FERET – Classical face recognition dataset.
- Deng Cai’s face dataset in Matlab Format – Easy to use if you want play with simple face datasets including Yale,
ORL, PIE, and Extended Yale B. - SCFace – Low-resolution face dataset captured from surveillance cameras.
Handwritten Digits
- MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.
Pedestrian Detection
- Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for
about 2.3K unique pedestrians. - INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
- ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
- TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
- PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
- USC Pedestrian Dataset – Small dataset captured from surveillance cameras.
Generic Object Recognition
- ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
- Tiny Images – 80 million 32x32 low resolution images.
- Pascal VOC – One of the most influential visual recognition datasets.
- Caltech 101 / Caltech
256 – Popular image datasets containing 101 and 256 object categories, respectively. - MIT LabelMe – Online annotation tool for building computer vision databases.
Scene Recognition
- MIT SUN Dataset – MIT scene understanding dataset.
- UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.
Feature Detection and Description
- VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor
an evaluation framework.
Action Recognition
- Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets
for action recognition.
RGBD Recognition
- RGB-D Object Dataset – Dataset containing 300 common household objects
state-of-the-art implementations related to visual recognition and search的更多相关文章
- Image Processing and Analysis_8_Edge Detection:Edge and line oriented contour detection State of the art ——2011
此主要讨论图像处理与分析.虽然计算机视觉部分的有些内容比如特 征提取等也可以归结到图像分析中来,但鉴于它们与计算机视觉的紧密联系,以 及它们的出处,没有把它们纳入到图像处理与分析中来.同样,这里面也有 ...
- Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io/ 里面有很多相当好的文章 http://cs231n.github.io/convolutional-networks/ Table of Cont ...
- 大规模视觉识别挑战赛ILSVRC2015各团队结果和方法 Large Scale Visual Recognition Challenge 2015
Large Scale Visual Recognition Challenge 2015 (ILSVRC2015) Legend: Yellow background = winner in thi ...
- 论文笔记之: Bilinear CNN Models for Fine-grained Visual Recognition
Bilinear CNN Models for Fine-grained Visual Recognition CVPR 2015 本文提出了一种双线性模型( bilinear models),一种识 ...
- CNN for Visual Recognition (01)
CS231n: Convolutional Neural Networks for Visual Recognitionhttp://vision.stanford.edu/teaching/cs23 ...
- 【论文阅读】Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
导读: 本文为论文<Deep Mixture of Diverse Experts for Large-Scale Visual Recognition>的阅读总结.目的是做大规模图像分类 ...
- 目标检测--Spatial pyramid pooling in deep convolutional networks for visual recognition(PAMI, 2015)
Spatial pyramid pooling in deep convolutional networks for visual recognition 作者: Kaiming He, Xiangy ...
- A Theoretical Analysis of Feature Pooling in Visual Recognition
这篇是10年ICML的论文,但是它是从原理上来分析池化的原因,因为池化的好坏的确会影响到结果,比如有除了最大池化和均值池化,还有随机池化等等,在eccv14中海油在顶层加个空间金字塔池化的方法.可谓多 ...
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Kaiming He, Xiangyu Zh ...
随机推荐
- WPF界面设计技巧(5)—自定义列表项呈现内容
原文:WPF界面设计技巧(5)-自定义列表项呈现内容 接续上次的程序,稍微改动一下原有样式,并添加一个数据模板,我们就可以达成下面这样的显示功能: 鼠标悬停于文件列表项上,会在工具提示中显示图像缩略图 ...
- swift 新功能介绍
原文链接:http://www.cocoachina.com/applenews/devnews/2014/0617/8857.html 假设你和我一样,准备好好看看苹果的 Keynote,兴奋地准备 ...
- as 的妙用
个人理解:as跟is is 相当于判断里的“==” 是与否 if(e.OriginalSource is Button) as 一般用来转换另一种object e.OriginalSource as ...
- ASP.NET关于Login控件使用,LoginView 控件,CreateUserWizard 控件
原文:ASP.NET关于Login控件使用,LoginView 控件,CreateUserWizard 控件 Login控件它是属于Membership服务的一部分,必须配置Membership提供程 ...
- JavaMail学习笔记(七)、帐号激活与忘记密码 实例(zhuan)
一.帐户激活 在很多时候,在某些网站注册一个用户之后,网站会给这个用户注册时填写的email地址发送一封帐户激活邮件,这封邮件的内容就是一个激活帐户的链接和一段简短的文字描述,如果用户没有去邮箱将 ...
- 全面认识Eclipse中JVM内存设置(转)
这里向大家描述一下Eclipse中如何进行JVM内存设置,JVM主要管理两种类型的内存:堆和非堆.简单来说堆就是Java代码可及的内存,是留给开发人员使用的:非堆就是JVM留给自己用的,所以方法区.J ...
- 状态压缩dp(hdu2167,poj2411)
hdu2167 http://acm.hdu.edu.cn/showproblem.php?pid=2167 给定一个N*N的板子,里面有N*N个数字,选中一些数字,使得和最大 要求任意两个选中的数字 ...
- Asp.net网站的简单发布
概述 网站是由一个个页面组成的,是万维网具体的变现形式,关于万维网,网页的方面的理论知识,大家可以看一看这篇博客:万维网文档,在这里就不多说了.网站的发布要到达的一个目的就是,别人可以通过浏览器访问该 ...
- 呈现怎样的香蕉饼路线Android系统
无话可说,该系统的第一版,Android有的还可以,路由设置确实有闪光现象背.与其他的稳定版本发布,被能够机顶盒和路由组合.其次是SSD,还是很不错的. 硬件 watermark/2/text/aHR ...
- 阶乘因式分解(一)(南阳oj56)
阶乘因式分解(一) 时间限制:3000 ms | 内存限制:65535 KB 难度:2 描写叙述 给定两个数m,n,当中m是一个素数. 将n(0<=n<=10000)的阶乘分解质因数, ...