Category: Computer vision

[Paper published] Check out our new paper for image classification with great results using only small sets of training data

Do you have great idea(s) using machine learning but stopped by the fact that you do not have enough training (image) data? ?  Check out our newly accepted KDD workshop paper for a novel solution.

New KDD 2019 MLG (the 15th International Workshop on Mining and Learning with Graphs) Workshop  paper for computer vision and image analysis led by Liping has been accepted:

Image classification using topological features automatically extracted from graph representation of images. 

A PDF of the paper can be found at the Workshop website or HERE. 

BibTeX Entry:

@inproceedings{mlg2019_7,
title={Image classification using topological features automatically extracted from graph representation of images},
author={Yang, Liping and Oyen, Diane and Wohlberg, Brendt},
booktitle={Proceedings of the 15th International Workshop on Mining and Learning with Graphs (MLG)},
year={2019}
}

 

Check out this page for Liping’s more publications.

 

[Paper published] Check out our new computer vision and image analysis paper for skeleton extraction

New CVPR 2019 Workshop paper for computer vision and image analysis led by Liping has been published:

A Novel Algorithm for Skeleton Extraction From Images Using Topological Graph Analysis. 

A PDF of the paper can be found HERE. (Check HERE if it is not retrievable on the http://openaccess.thecvf.com) [Acceptance rate < 10/32 = 31.25%]

4. Yang, L. and Worboys, M. Generation of navigation graphs for indoor space. International Journal of Geographical Information Science, 29(10): 1737-1756, 2015. [Click here (PDF) to download a draft of this paper]

 

Check out this page for Liping’s more publications.

WACV (IEEE Winter Conference on Applications of Computer Vision)

Overview

  • Winter Conference on Applications of Computer Vision (WACV) is the IEEE’s and the PAMI-TC’s premier meeting on applications of computer vision.
  • WACV is a full-fledged IEEE conference which covers all areas of computer vision.
  • It is always held in USA.

Current and Past WACV Conferences

  • WACV 2020 (held March, 2020 at The Westin Snowmass Resort in Snowmass village, Colorado, USA)

 

  • WACV 2019 (held January, 2019 at Hilton Waikoloa Village, Hawaii, USA)

 

 

  • WACV 2018 (held March, 2018 at Harvey’s Casino in Lake Tahoe, NV/CA)

      • Sponsors

  • WACV 2017 (held on March 27-29, 2017 at the Hyatt Vineyard Creek Hotel, Santa Rosa, CA, USA)

 

  • WACV 2016 (held March 7-9, 2016 at the Crowne Plaza resort in Lake Placid, NY, USA)

 

 

  • WACV 2015 (held  January 6-9, 2015 at Waikoloa Beach Marriott Resort & Spa, Big Island, Hawaii)

      • WACV2015 Main conference (People)

      • Acceptance rate

        • Paper acceptance statistics:
          425 paper submitted
          156 accepted (35 in round 1, 121 in round 2)
          36.7% accept rate

 

  • TBA

Computer Vision (CV) Resources: CV Terms and Algs

This page provides some fundamental and essential computer vision (CV) related terms, concepts, and algorithms.

Terms/Concepts/Algorithms

In computing, indexed color is a technique to manage digital images‘ colors in a limited fashion, in order to save computer memory and file storage, while speeding up display refresh and file transfers. It is a form of vector quantization compression.

When an image is encoded in this way, color information is not directly carried by the image pixel data, but is stored in a separate piece of data called a palette: an array of color elements. Every element in the array represents a color, indexed by its position within the array. The individual entries are sometimes known as color registers. The image pixels do not contain the full specification of its color, but only its index in the palette. This technique is sometimes referred as pseudocolor[1] or indirect color,[2] as colors are addressed indirectly.

Perhaps the first device that supported palette colors was a random-access frame buffer, described in 1975 by Kajiya, Sutherland and Cheadle.[3][4] This supported a palette of 256 36-bit RGB colors.

 

An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

The first microcomputer-based image database retrieval system was developed at MIT, in the 1990s, by Banireddy Prasaad, Amar Gupta, Hoo-min Toong, and Stuart Madnick.[1]

A 2008 survey article documented progresses after 2007.[2]

CBIR — the application of computer vision to the image retrieval. CBIR aims at avoiding the use of textual descriptions and instead retrieves images based on similarities in their contents (textures, colors, shapes etc.) to a user-supplied query image or user-specified image features.

List of CBIR Engines – list of engines which search for images based image visual content such as color, texture, shape/object, etc.

Further information: Visual search engine and Reverse image search

Image collection exploration is a mechanism to explore large digital image repositories. The huge amount of digital images produced every day through different devices such as mobile phones bring forth challenges for the storage, indexing and access to these repositories. Content-based image retrieval (CBIR) has been the traditional paradigm to index and retrieve images. However, this paradigm suffers of the well known semantic gap problem. Image collection exploration consists of a set of computational methods to represent, summarize, visualize and navigate image repositories in an efficient, effective and intuitive way.[1]

Summarization

Automatic summarization consists in finding a set of images from a larger image collection that represents such collection.[2] Different methods based on clustering have been proposed to select these image prototypes (summary). The summarization process addresses the problem of selecting a representative set of images of a search query or in some cases, the overview of an image collection.

References

 

Install OpenCV3 into virtualenv on Mac

This post introduces how to install OpenCV3 into a virtualenv on Mac.

If you have not setup virtualenv on your mac, check my post here to do that before you proceed the tutorial in this post.

Let us get started.

Step 1: Activate your virtualenv in your terminal

for example:

$ source ~/ipy-jupyter-venv3/bin/activate  

(ipy-jupyter-venv3)$  # Your prompt should change

Step 2: Install OpenCV (modules) according to your needs

  1. If you need only main modules, in your activated virtualenv in your terminal, run the following
(ipy-jupyter-venv3)$ pip3 install opencv-python 

2. If you need both main and contrib modules (check extra modules listing from OpenCV documentation),  in your activated virtualenv in your terminal, run the following

(ipy-jupyter-venv3)$ pip3 install opencv-contrib-python 

Step 3: Test whether openCV is installed correctly

To test whether OpenCV installed correctly into your virtualenv, in your terminal type in those command below in bold.

(ipy-jupyter-venv3) liping$ python3.6

Python 3.6.5 (default, Mar 30 2018, 06:41:53)

[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)] on darwin

Type “help”, “copyright”, “credits” or “license” for more information.

>>> import cv2

>>> cv2.__version__

‘3.4.3’

>>

Step 4 (optional): To uninstall opencv inside virtualenv, following the command below according to how you installed it.

(ipy-jupyter-venv3) Liping:~$ pip3 uninstall opencv-python 

or

(ipy-jupyter-venv3) Liping:~$ pip3 uninstall opencv-contrib-python

 

Summary:

In this post, you learned how to install and uninstall OpenCV into your virtualenv.

Computer Vision (CV) resources

This page provides some useful resources about computer vision (CV).

(Stay tuned, as I will update the content on this  page while I plow and grow in my deep learning garden:))

  • Books

  • Szeliski, R. (2010). Computer vision: algorithms and applications. Springer Science & Business Media. (book website; PDF link) .    [MIT, UCBerkeley, Princeton CV course primary textbook]
  • Forsyth, D. A., & Ponce, J. (2012). Computer Vision: A Modern Approach (2nd Edition). Prentice Hall. The first edition with the same book title was published on 2003.     [MIT, UCBerkeley CV course secondary textbook]
  • Prince, S. J. (2012). Computer vision: models, learning, and inference (book website; PDF link). Cambridge University Press.
  • Fisher, R. B., Breckon, T. P., Dawson-Howe, K., Fitzgibbon, A., Robertson, C., Trucco, E., & Williams, C. K. (2014). Dictionary of computer vision and image processing. John Wiley & Sons.
  • Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge university press.
  • Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. MIT Press. Cambridge, Massachusetts. (book website)
  • Bishop, C. (2006).Pattern Recognition and Machine Learning, Springer. (book website, PDF link)
  • Wilson, J. N., & Ritter, G. X. (2000). Handbook of computer vision algorithms in image algebra (2nd edition). CRC press.

  • Top and major CV conferences 

  • Top and major CV journals

  • IEEE PAMI (Transactions on Pattern Analysis and Machine Intelligence) —  (1979 – present, monthly) [SCI indexed] (the best journal in computer vision)
  • IJCV (International Journal of Computer Vision)– (1987 – present) [SCI indexed; Springer]

(IJCV is typically considered on par with T-PAMI which is the best journal in computer vision. Review time should be ~4-5 months for first set of reviews and ~12-14 mo for final publication. A good way to get an estimate of time would be to browse through the recently published articles here –they list submitted date and publication date)

 

References and Further Reading List