Towards Data Science

Tracking street art with machine learning — updates

November 8, 2018 by rememberlenny

Mural from Reyes, Revok and Steel from MSK (https://www.fatcap.com/live/revok-steel-and-reyes.html)

Thank you for following the Public Art[⁰] project for building a genealogy around street art, using machine learning. This project is aiming to create a central place for documenting street art from around the world, and use modern image analysis techniques to build a historical reference of public art for the future.

As a quick update, this project began in 2014, during Gary Chou’s Orbital bootcamp [¹], during which I built a series of small projects exploring how software and graffiti can co-exist in experimental side-projects. One of those side-projects was a experiment in crawling Instagram images and building out a iOS for browsing street art that is near you. This app, which is no longer fully functional, is still on the iOS app store [²].

This past August, I began participating in the Pioneer tournament, which is a monthly tournament built around a community of creative young people working on interesting projects around the globe. I decided to restart the project around documenting graffiti, by integrating my familiarity with machine learning.

In September, I ran a “Quickstarter”, which is a $100 Kickstarter project, and surprisingly, beyond friends, found a number of complete strangers who were interested in the project [³]. This project gave me confidence to further explore how street art and software could co-exist.

During this same project, I began continuing to crawl more images from public resources online, and similarly found a huge issue with my old methods. While I could still crawl Instagram, similarly to how I did in 2014, much of the metadata I needed for historical purposes was no longer available. Specially, I didn’t have access to the geographical data that was key for making these images useful. I wrote briefly on this here: On the post-centralization of social media [⁴].

PublicArt.io current website’s functional prototype

Since then, I have moved my focus away from building tools to crawl public resources and toward building a foundation on to which publicly documented street art can be stored online.

This will emulate the many photo sharing services already online, inspired by Flickr, Instagram, Imgur, to name a few. The focus of the service will be solely to document street art, help collect images on art pieces, view artists work, and provide public access to this data.

I am proud to announce that Tyler Cowen [⁵], of the Mercatus Center from George Mason University [⁶], has extended his Emergent Ventures fellowship to my project [⁷].

Although this project was originally personally funded, I feel a greater confidence behind being able to extend my time into building out tools. With this grant, I am confident I am building something that has the ability to sustain its own costs and prove its worth.

Prior to my current state of exploration, I was experimenting with applying image feature extraction tools with embedding analysis techniques to compare how different street art pieces are similar or different. To over-simplify and explain briefly: Image feature extraction tools can take an image and quantify the presence of a single parameter, which represents a feature [⁸].

Im analyzing graffiti images with machine learning techniques to build a genealogy of graffiti.

I use a convolutional neural network based feature extraction and encoded results. This shows 5,623 photos cluster the similar artists based on 25,088 dimensions. pic.twitter.com/BcYLyCMZSq

— Lenny Bogdonoff (@rememberlenny) September 10, 2018

The parameter can be then simplified into a single number, which then can be compared across images. With machine learning tools, specifically the Tensorflow Inception library [⁹], tens of thousands of features can be extracted from a single image, then used to compare against the similar features from other images.

By taking these embeddings, I was able to generate very interesting three-dimensional space visuals that showed how certain artists are similar or different. In the most basic cases, stencil graffiti was mapped to the same dimensional space, while graffiti “bombs” or larger murals would map to similar multi-dimensional space respectively [¹⁰].

Using the hundreds of thousands of images I was able to crawl from Instagram before the geographical data was made inaccessible, I analyzed how the presence of street art around the world, over time [¹¹].

Video: 30 seconds of animated geolocation data for street art images taken around the world over time pic.twitter.com/YzTjdN2sLY

— Lenny Bogdonoff (@rememberlenny) November 2, 2018

This data, which was no longer associated to the actual images that were originally indexed — due to Instagram’s change in policy — provided insight into the presence of street art and graffiti around the world.

Interestingly, the image frequency also provided a visual which eludes to an obvious relationship between urban centers and street art. If this was analyzed further there may be clear correlations between street art and real estate value, community social ties, political engagement, and other social phenomena.

In the past few days, I have focused on synthesizing the various means with which I expect to use machine learning for analyzing street art. Because of the media’s misrepresentation of artificial intelligence and the broad meaning of machine learning in the technical/marketing field, I was struggling with what I meant myself.

Prior to this project’s incarnation, I had thought it would be possible to build out object detection models to recognize different types of graffiti in images. For example, an expression of vandalism is different than a community sanctioned mural. I also imagined it would be possible to build out ways of identifying specific letters in larger letter-form graffiti pieces. I believe it would be interesting to combine the well defined labels and data set with a variational auto-encoder to generate machine learning based letter-form pieces.

Going further, I thought it would be possible to use machine learning to detect when an image in a place was “new”, based on it not having been detected in previous images from a specific place. I thought it would also be interesting to find camera feeds to railway cars traveling across the US and build out a pipeline for capturing the graffiti on train cars, identifying the train cars serial number, and tracking how train cars and their respective art traveled the country.

All of the above points are practical expressions of the machine learning based analysis techniques.

While these are interesting projects, I have synthesized my focus to the following six points for the time being: recognizing artists work, tracking similar styles/influences, geo-localize images [¹²], categorize styles, correlate social phenomena, and find new art. Based on tracking the images, the content, the frequency of image images, and making this data available to others, I believe street art can create more value as it is and gain even more respect.

Based on recent work, I have gotten a fully functional application working that allows for users to create accounts, upload images, associate important metadata (artist/location/creation data) to images. While the user experience and design is not anywhere that I would be proud of, I will be moving forward with testing the current form with existing graffiti connoisseur.

As I continue to share about this project, please reach out if you have any interest directly or would like to learn more.

[0]: https://www.publicart.io
[1]: https://orbital.nyc/bootcamp/
[2]: http://graffpass.com
[3]: https://www.kickstarter.com/projects/rememberlenny/new-public-art-foundation-a-genealogy-of-public-st/updates
[4]: https://medium.com/@rememberlenny/on-the-instagram-api-changes-f9341068461e
[5]: http://marginalrevolution.com
[6]: https://mercatus.org/
[7]: https://marginalrevolution.com/marginalrevolution/2018/11/emergent-ventures-grant-recipients.html
[8]: https://en.wikipedia.org/wiki/Feature_extraction
[9]: https://www.tensorflow.org/tutorials/images/image_recognition
[10]: https://twitter.com/rememberlenny/status/1038992069094780928
[11]: Geographic data points — https://twitter.com/rememberlenny/status/1058426005357060096
[12]: Geolocalization — https://twitter.com/rememberlenny/status/1053626064738631681

Two months exploring deep learning and computer vision

December 28, 2017 by rememberlenny

Repost from Medium

I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this growing sphere exciting, but did not have any contextual experience working with these technologies. I am embarking on a two year journey to explore this field. If you haven’t read it already, you can see Part 1 here: From webdev to computer vision and geo.

I️ ended up getting myself moving by exploring any opportunity I️ had to excite myself with learning. I wasn’t initially stuck on studying about machine learning, but I wanted to get back in the groove of being excited about a subject. I️ kicked off my search by attending a day-long academic conference on cryptocurrencies, and by the time the afternoon sessions began, I realized machine learning and computer vision was much more interesting to me.

Getting started

I️ kick-started my explorations right around the time a great book on the cross section of deep learning and computer vision was published. The author, Adrian Rosebrock from PyImageSearch.com, compiled a three volume masterpiece on the high level ideas and low level applications of computer vision and deep learning. While exploring deep learning, I️ encountered numerous explanations of linear regression, Naive Bayesian applications (I️ realize now that I️ have heard this name pronounced so many different ways), random forest/decision tree learning, and all the other things I’m butchering.

I️ spent a few weeks reading the book and came away feeling like I️ could connect all the disparate blog posts I have read up to now to the the array of mathematical concepts, abstract ideas, and practical programming applications. I read through the book quickly, and came away with a better sense of how to approach the field as a whole. My biggest takeaway was coming to the conclusion that I️ wanted to solidify my own tools and hardware for building computer vision software.

Hardware implementation

I️ was inspired to get a Raspberry Pi and RPI camera that I️ would be able to use to analyze streams of video. Little did I know that setting up the Raspberry Pi would take painfully long. Initially, I️ expected to simply get up and running with a video stream and process the video on my computer. I️ struggled with getting the Raspberry Pi operating system to work. Then, once I️ realized what was wrong, I️ accidentally installed the wrong image drivers and unexpectedly installed conflicting software. The process that I️ initially thought would be filled with processing camera images, ended up becoming a multi hour debugging nightmare.

So far, I️ have realized that this is a huge part getting started with machine learning and computer vision “stuff” is about debugging.

Step 1.Get an idea.
Step 2. Start looking for the tools to do the thing.
Step 3. Install the software needed.
Step 4. Drown in conflicts and unexpected package version issues.

My original inspiration behind the Raspberry Pi was the idea of setting up a simple device that has a camera and GPS signal. The idea was based around thinking about how many vehicles in the future, autonomous or fleet vehicles, will need many cameras for navigation. Whether for insurance purposes or basic functionality, I️ imagine that a ton of video footage will be created and used. In that process, there will be huge repositories of media that will go unused and become a rich data source for understanding the world.

I️ ended up exploring the Raspberry Pi’s computer vision abilities, but never successfully got anything interesting working as I’d hoped. I️ discovered that there are numerous cheaper Raspberry Pi-like devices, that had both the interconnectivity and the camera functionality in a smaller PCB board than a full size Raspberry Pi. Then I️ realized that rather than going the hardware route, I️ might as well have used an old iPhone and developed some software.

My brief attempt at exploring a hardware component of deep learning made me realize I should stick to software where possible. Including a new variable when the software part isn’t solved just adds to the complexity.

Open source tools

In the first month of looking around for machine learning resources, I found many open source tools that make getting up and running very easy. I knew that there were many proprietary services provided by the FANG tech companies, but I wasn’t sure how they competed with the open source alternatives. The image recognition and OCR tools that can be used as SAAS tools from IBM, Google, Amazon, and Microsoft are very easy to use. To my surprise, there are great open source alternatives that are worth configuring to avoid unnecessary service dependence.

For example, a few years ago, I launched an iOS application to collect and share graffiti photos. I was indexing images from publicly available API’s with geotagged images, such as Instagram and Flickr. Using these sources, I used basic features, such as hashtags and location data, to distinguish if images were actually graffiti. Initially, I began pulling thousands of photos a week, and soon scaled to hundreds of thousands a month. I quickly noticed that many of the images I indexed were not graffiti and instead were images that would be destructive to the community I was trying to foster. I couldn’t prevent low-quality photos of people taking selfies or poorly tagged images that were not safe for work from loading in people’s feeds. As a result, I decided to shut down the overall project.

Now, with the machine learning services and open source implementations for object detection and nudity detection, I can roll my own service that easily checks each of the photos that get indexed. Previously, if I paid a service to do that quality checking, I would have been racking up hundreds of dollars if not thousands of dollars in API charges. Instead, I can now download an AMI from some “data science” AWS box and create my own API for checking for undesired image content. This was out of reach for me, even just two years ago.

On a high level, before undergoing this process, I felt like I theoretically understood most of the object recognition and machine learning processes. After beginning the process of connecting the dots between all the machine learning content I had been consuming, I feel like I am much more clear on what concepts I need to learn. For example, rather than just knowing that linear algebra is important for machine learning, I now understand how problems are broken into multidimensional array/matrices and are processed in mass quantities to look for patterns that are only theoretically representable. Before, I knew that there was some abstraction between features and how they were represented as numbers that could be compared across a range of evaluated items. Now I understand more clearly how dimensions, in the context of machine learning, are represented by the sheer fact that there are many factors that are directly and indirectly correlated to one another. The matrix math that the multidimensional aspects of feature detection and evaluation is still a mystery to me, but I am able to understand the high level concepts.

Concretely, the reading of Adrian Rosebrock’s book gave me the insight to decode the box-line diagrams of machine learning algorithms. The breakdown of a deep learning network architecture is now somewhat understandable. I am also familiar with the datasets (MNIST, CIFAR-10, and ImageNet) that are commonly used to benchmark various image recognition models, as well as the differences between image recognition models (such as VGG-16, Inception, etc).

Timing — Public Funding

One reason I decided machine learning and computer vision are important to learn now is related to a concept I learned from the book: Areas with heavy government investment in research are on track to have huge innovation. Currently, there are hundreds of millions of dollars being spent on research programs in the form of grants and scholarships, in addition to the specific funding being allocated to programs for specific machine learning related projects.

In addition to government spending, publicly accessible research from private institutions seems to be growing. The forms of research that currently exist, coming out of big tech companies and public foundations, are pushing forward the entire field of machine learning. I personally have never seen the same concentration of public projects funded by private institutions in the form of publications like distill.pub and collectives like the OpenAI foundation. The work they are putting out is unmatched.

Actionable tasks

Reviewing the materials I have been reading, I realize my memory is already failing me. I’m going to do more action-oriented reading from this point forward. I have a box with GPUs to work with now, so I don’t feel any limitations around training models and working on datasets.

Most recently, I attended a great conference on Spatial Data Science, hosted by Carto. There, I became very aware of how much I don’t know in the field of spatial data science. Before the conference, I was just calling the entire field “map location data stuff”.

I’ll continue making efforts to meet up with different people I find online with similar interests. I’ve already been able to do this with folks I find who live in New York and have written Medium posts relevant to my current search. Most recently, when exploring how to build a GPU box, I was able to meet a fellow machine learning explorer for breakfast.

By the middle of January, I’d like to be familiar with technical frameworks for training a model around graffiti images. I think at the very least, I want to have a set of images to work with, labels to associate the images to, and a process for cross-checking an unindexed image against the trained labels.

Two months exploring deep learning and computer vision

December 20, 2017 by rememberlenny

I’ve been reading/note taking is using an iPad Pro and LiquidText

—

Getting started

My new book, #DeepLearning for Computer Vision with #Python, has been OFFICIALLY released! Grab your copy here: https://t.co/rQgpAflp52 pic.twitter.com/fKEyf8i2fR

— PyImageSearch (@PyImageSearch) October 17, 2017

Hardware implementation

So far, I️ have realized that this is a huge part getting started with machine learning and computer vision “stuff” is about debugging.

Step 1.Get an idea.
Step 2. Start looking for the tools to do the thing.
Step 3. Install the software needed.
Step 4. Drown in conflicts and unexpected package version issues.

https://aiyprojects.withgoogle.com/vision#list-of-materials

Open source tools

Overview

The previously illegible network architecture graphs are now seemingly approachable.

Timing — Public Funding

Example of pix2pix algorithm applied to “cat-ness”. https://distill.pub/2017/aia/

Actionable tasks

Thanks to Jihii Jolly for correcting my grammar.

From webdev to computer vision and machine learning

October 13, 2017 by rememberlenny

For the past four years, I’ve had the notion that I was going to start a company. The underlying feeling was that I wanted to work on something that I could passionately take full responsibility for. Although this has been a constant desire, I have not taken the concrete steps to make this a reality. Instead, I have developed a career writing software and learning the contracting market. Rather than develop a business plan and try to raise money, I have built and released many side-projects. Each project has given me greater understanding about a technology or field of interest.

I have actively thrown myself into whatever tasks and opportunities I had in front of me. As a result, I’ve been able to meet numerous talented and amazing people in the media, art, tech, and social-cause oriented spaces.

I started web development around the time mobile development became important. I watched JavaScript explode from a complementary skillset with html and css, to the primary language needed to understand a seemingly unending amalgamation of frameworks.

The interesting projects I would hear about were related to building new social networks or building off of existing ones. Mobile location technology was all the rage, but not fully matured and photo sharing services were growing in influence. I even made an photo-based iOS application myself, while making a very conscious effort to not needlessly recreate Instagram.

Whats next

Most recently, I‘ve been really excited about mapping technologies and the emergence of more real-time related applications. When I noticed the popularity of “big data” open source projects like Hadoop and Spark, I didn’t feel like I had anything to experiment with. I tried my hand at creating an online market-platform site. I saw the rise of monthly-box-for-X services and considered how hard it would be to create a worthwhile logistics service that could fall under a monthly-box-for-X, or a more attractive uber-for-X. I even played with what is possible with IoT devices and how I might go about producing something if I validated an idea worth manufacturing. I researched bluetooth specifications, power delivery mechanisms, and wondered what interesting art-oriented applications I could scrap together. Overall, I never committed enough to fully see the fruits of my exploration. Yet, all the processes were valuable for my own growth.

Now, it feels as if there is even more to explore, and while I don’t know how, I can clearly feel that the path forward will be greater in scale.

I’m excited about the blockchain, machine learning, and new augmented/virtual reality. Of all new emerging technologies, I’ve spent the most time trying to understand and utilize machine learning for practical purposes. While I understand the blockchain in theory, I don’t feel any deep affinity for the product. I’m not driven by the anti-establishment/pro-sovereignty ideologies that fuel the crypto culture. I also don’t find the AR or VR space as interesting as those I know who have gone “all in”. I like the idea of a physical space complemented with virtual layers, but haven’t had any “Aha!” moments around how to execute the process.

Computer Vision

Image analysis and the seemingly interesting data that can be extracted through machine learning continues to pique my interest. Further, the horizon of changes in transportation (read: self-driving cars) gives me conviction that real-time location-dependent image analysis data is going to have growing importance.

Academia

I feel there is interesting work being done in the academic and private sectors for both of these areas. In the academic sector, I previously saw reputable universities doing a lot of image-oriented work that wasn’t immediately interesting. The highly theoretical work around compression, color, or the like are not appealing to me. The work around medical image analysis seems like a large field, but is completely unattractive in my mind. I saw numerous research projects around the field of 2D-image-to-3D space translation. In an isolated study, these image-to-X projects aren’t interesting, but the applications of these studies in the real world seem worth exploring.

I would like to spend a clear amount of time to fully grok the academic landscape of image-related research being done. I think reviewing the top universities around the United States as well as around the world would be highly enlightening for myself and whatever I may do.

I’ve tried to determine if returning to school for a masters program in further study would be worthwhile. From my limited exposure, I haven’t found a reason why this would be critical, but I can imagine the fixed time to focus on an isolated topic would be highly beneficial. The opportunity to surround myself with likeminded people seems worthwhile.

Private sector

I have been continually fascinated with the idea of using a camera as a multi-use sensor. The software-oriented computer vision tasks that exist seem highly unexplored. This is something I would like to spend more time to map out for myself. The newer interesting applications, such as self-driving cars, and the improvements around object recognition is fascinating. I know there are tons of other areas of interest that make this field worth exploring. I want to learn about everything from satellites to security cameras, advertising to real-estate, self-driving fleet vehicles to augmented reality cameras.

From the “look what may exist 20 years from now, and ask yourself how to apply it to today” perspective, this seems like the most exciting field to me. I would like to commit significant time to understanding the future-to-be opportunities, ecosystem, and strengths/weaknesses of this space when applied to real world problems.

Beyond technical exposure, the philosophical implications of having a society devoid of privacy is scary. Practical commercial value feels like an area that will continue to develop into the future and with it, public understanding. Rather than waiting for the Edward Snowden big-data equivalent moment for camera technology, as Gary Chou succinctly put it, the social understanding and regulatory boundaries need to be ironed out. That being said, cameras will inevitably be everywhere and data from them will be mined by private companies. I’d like to be on the side of determining the positive value that can be created here.

Location

Mapping technology has continued to pique my interest. I started out wanting to learn more when I was doing graffiti and wondering how to avoid getting caught. “Writers” would talk about how the police used mapping technology for pinpointing artists. The running joke was that an artist’s biggest fan was the police. They had the most photos and biggest record of all the work done by a person. I recall how cities would catch artists by mapping all the reported “tags” from a single person. Through mapping the locations, the artist’s home neighborhood could often be inferred, and the overall investigation significantly narrowed. This was 15 years ago. I have no idea what the official process of this kind of data analysis is called, but I’m sure it has improved since then.

I can imagine the value when applying the same modes of analysis to any other dataset, be it social media data or commercial behavior. I’d like to deepen my understanding around this area. I imagine there is a lot to be learned around the geospatial analysis often used in natural resource prospecting. I want to learn more about how to make use of satellites. This is a superpower that was never accessible in the past, unless you were a nation-state. Hedge funds are now using satellite imagery to analyze the supply-demand of the retail industry by analyzing parking lots and transport vehicles across the world. The parallels here to other industries seem equally endless and untapped. With the adoption of cameras everywhere on the ground, I imagine the same means of analysis over time to be highly valuable.

I want to commit more time to understanding the overall problems in the space surrounding geospatial data over time. I imagine many opportunities when this is teamed up with image analysis over time and in real-time. I’d like to further understand the private sector applications and the academic fields best-suited to implement these technologies.

Overall

I slipped into programing at a time that made it possible for me to have one step ahead of the then-current incumbents. I feel like now, again, I am in a place where my past experience gives me unique insight into what I could further explore. The fields that are of particular interest, image analysis and geospatial data, seem worth understanding and are now as approachable as they will ever be. Given that my personal life and career is financially secure, I will spend the next two years personally exploring and understanding these fields. My goal is to deepen my ability to see how these fields can be used for commercial gain. I will deepen my outlook into the forefront of academic research, private sector practices. Ideally, through this process of exploration, I can document my findings for others who also find themselves interested but unsure how to further explore these areas.

Thanks to Gary Chou for suggesting to write this and help with synthesizing ideas. And thanks to Jihii Jolly for fixing the editing nightmare.