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:

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:

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).

Part-Based Models

Attributes and Semantic Features

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

3D Recognition

Action Recognition


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

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

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

Feature Detection and Description

  • VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor
    an evaluation framework.

Action Recognition

RGBD Recognition

state-of-the-art implementations related to visual recognition and search的更多相关文章

  1. 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 ...

  2. 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 ...

  3. 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 ...

  4. 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 ...

  5. CNN for Visual Recognition (01)

    CS231n: Convolutional Neural Networks for Visual Recognitionhttp://vision.stanford.edu/teaching/cs23 ...

  6. 【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 ...

  7. 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 ...

  8. 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 ...

  9. 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

  1. 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 ...

  2. 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 ...

  3. 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 ...

  4. 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 ...

  5. 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 ...

  6. 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 ...

  7. 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 ...

  8. 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 ...

  9. 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 ...

  10. 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, ...