I noticed a very interesting initiative recently Project Geograph: Photograph Every Grid Square.

This project is working towards collecting and making available images depicting the geography of every square kilometre of the British Isles. This ambitious project seems to be progressing very well, with many good quality images loaded to the website.

Already over 8,900 contributors have submitted nearly 1,500,000 images, with an average of 5 images associated to each geographic square across England, Wales, Scotland and Ireland.  This is a great resource, preserving in amazing detail what the British Isles looked like at the start of the 21st Century.  This is also a wonderful way to learn about the geography of these amazing islands and to dig deeply into their hills, valleys, towns and villages.  This is also a superb source for genealogists looking at how a particular part of the British Isles looks today.

Back in 2007 I attended the Blogs and Social Media Conference 2.0 in London.  One presentation which has stayed in my mind since then, was Lee Bryant's, "Engaging with Passionates". In his exceptional presentation Lee described a ground-breaking social networking case study and talked about the energy that can be released when organisations successfully tap into a group of people who are truly passionate about a given topic.

I think you'd be hard pressed to find a better example of the power of passionates than the Geograph Project.  Looking at the number of contributors, the amount of the British Isles covered, and the quality of the photography and metadata created, makes a clear point - find people who are passionate about a topic, people who are committed to a hobby or interest, engage them in the right way and they will deliver time and again.

I wish everyone associated with the Geograph Project all the luck in the world, may they stay passionate and committed to what they do, and may their project benefit from their commitment.

Oh, and if you like what you see, submit a photograph, or start a similar initiative.

Ian

I spent Monday and Tuesday of this week at the fascinating ISKO Content Architecture Conference.

On Monday I gave a presentation on, "Still Digital Images - the hardest things to classify and find."
My presentation looked at the image market and the ways in which images can be annotated - or is that processed, classified, categorized, tagged, keyworded… We need a controlled vocabulary to controlled the vocabulary of controlled vocabulary! 

Welcome back to my occasional image classification series.

The last time I raised the topic of image classification I discussed the basic attributes of images. This time I want to focus on the thornier issue of the content, or concepts, depicted in them.

There is a danger of treating an image like a piece of text and classifying its attributes: Who created it? When? What techniques were used? Then writing a title or caption and leaving it at that. Sometimes little more need be done to a document than record this kind of information, especially with free text searching, but lots more needs to be done to most images.

Image findability

Image findability is the process of using search and browse to access the images required. A major aspect of image findability relates to the things depicted in them. Image users often search for images based on the generic things in them and also the proper names of these things. Classifying images based on depicted content means considering anything and everything that is and can be depicted in an image. When considering this I like to focus my efforts on understanding the images I'm dealing with, the users who are trying to find and work with the images, and the ways in which these people need to search and browse for the images they need. After an assessment of these areas I then tailor my approach.

Broadly speaking people searching for depicted content are looking for a number of types:

• Places: cities, towns, villages, streets...
• Built works: parks, skyscrapers, cottages, walls, doors, windows...
• Topography: mountains, valleys...
• Groups and organisations: air forces, choirs, police departments...
• People: roles, occupations, ethnicity and nationality: mothers, doctors, Caucasians, French, Germans...
• Actions, activities and events: running, writing, laughing, smiling, birthdays, parties, book signings, meetings...
• Objects: a myriad of items...
• Animals and plants: common and scientific names...
• Anatomy and attributes of people, animals and plants: arms, legs, adults, leaves, trunks, paws, tails...
• Depicted text shown in images - often signs or writing shown in images...

Many of these generic types can also have proper named instances:

• Proper names of people, places, buildings, topography, organisations, animals etc

When dealing with depicted content I've found some of the biggest issues to be:

• Identification - knowing what is in an image
• Focus and specificity - knowing what to include and what to exclude
• Consistency - applying the same term in the same way for the same depicted content

Identification - knowing what is in an image

Depicted content is a relatively black and white area - a dog is depicted so a dog is tagged. However, it might sound a little weird, but working out what is actually in an image can be a lot harder than you think.

Take a look at the image "Do You Know What This Is?" by Sister72

This depicted content is fairly simple to see, but understanding what you're looking at is not that easy. Even if you know roughly what you're looking at, do you know what it's actually called?

One tip is to group similar images together when you're classifying them. Also, always start by assembling as much information as possible before you begin to classify images. It is especially important to gather together the information you have from the creator or custodians of the images.

Also important, when you have the luxury, is to get the image creator to add key metadata about the image at the point of creation, or soon after.

Focus and specificity

Knowing what to include and what to exclude, what to mention and what to ignore, is also much harder than it sounds.

Firstly, some image users will want a piece of depicted content tagged whenever it appears in an image, others will only want it tagged when the image shows a very good representation of that content, and of course many people will want something in between the two extremes.

Different users have different requirements. You need to understand the domain in which you're working and see the classification of depicted image content as supporting the needs of your users.

For example, Would you tag everything in this 'Messy Room' image?

What would you miss out and why?

Looking at the image of "Mountain Goats", from Thorne Enterprises

Would you tag this with goats as well as mountains? Would this be helpful?

Let's look at four images depicting windows:

'Window to the World'?,

'Portuguese Window'?, '

What Light Through Yonder Window Breaks'?

and

'Window'.

Looking at these, it soon becomes clear that even deciding to apply a simple term like 'Windows' is not always easy.

Would you apply 'Windows' to the image of the cat looking out of the window? Is a window actually depicted in that image? If the image wasn't tagged with 'Windows' how else would anyone find an image of a cat looking out of a window?

The other three images show windows as parts of buildings. but is a building always depicted? Deciding when to apply a building type or the name of a building can be hard. Should you do this every time a part of a building is shown? Only when the whole building is shown? When enough of the building is visible? Or when a section of the building that to most people would represent the build is visible? For example, what part of the Empire State Building would you consider to depict that building? Rarely does anyone see it all - how much is enough? Would you treat the images of windows in a similar way and classify them all with a building type of 'Houses', or would you ignore the structure and focus on the parts - the window, the roof?

Consistency

Achieving consistent application of terms to images revolves partly around clear term definitions, well defined application rules and guidelines, and a robust quality assurance process.

Term definitions are very important. Defining the meaning of a term, and ensuring the people choosing which term to assign understand that meaning, can be crucial to term application. For example, creating a term such as 'Bow' without defining its meaning is not going to make it easy to apply.

Application rules that are well considered, thorough and clear are also very useful. Even a simple concept often needs some form of guidance linked to it. I remember a while ago needing two terms, 'Indoors' and 'Outdoors' to allow users to find images of people who were outside and inside - a simple concept you might think, one that people often need, and one that's easy to apply - who'd need guidelines for that? However, it soon became clear that guidelines were needed after I received a series of interesting questions: Is being on a train indoors? Should studio shots always be considered indoors? Does every shot of a person have to have indoors or outdoors assigned to it? If not, when should this term be used and when not? Is this a focus issue? If so, how much of a location needs to be seen before Indoors or Outdoors is used. A clear set of application guidelines followed an interesting meeting!

Strong quality assurance processes are very valuable. People make mistakes and images generate interesting issues. Appointing staff to review a percentage of classification work based on clear guidelines, and then sharing findings with the people who assigned the terms to the images, is an important way of assessing how well the image classification is progressing and keeping a classification team synchronised.

Today I’ve talked a lot about content depicted in images, next time I’ll focus on abstract concepts which are related to an images ‘aboutness’.

I was very interested to see Google experimenting with visual similarity in still images, what I usually call Content Based Image Retrieval or CBIR.

Google Labs have just launched an image search function based on visual similarity - Google Similar Images. This new offering allows searchers to start with an initial image and then find other images that look like their example picture.

I've been reviewing these type of systems on and off since the early '90s. They've always offered much, but I never saw any evidence that the delivery matched the hype.

I've always found that using pictures instead of text to find images works best on simple 2d images: carpet patterns, trademarks, simple shapes, colours and textures. Finding objects in images was always a struggle, and looking for abstract concepts: fear, excitement, gloom, isolation, solitude.. was never been more than a vague possibility. Over the years a lot of work has been done in this area, and the search results I've seen have started to improve, but this technology is still young, and in my personal opinion still rarely delivers what most users want, need and expect.

Looking at Google Similar Images, I wonder how much of the back-end is pure content based image retrieval (CBIR), how much is using metadata in some way, and how the two are interacting? One thing that appears to be helping to often show a tight first page of results, is simply pulling the same image from different sites. I also noticed that the 'similar images' option is not available for all images - which makes me wonder why? Have some images been processed in ways that others haven't?

Diving right into the experience, I entered a query for a place in the UK and didn't see any image results with the 'Similar Images' option. I wonder whether this is to do with the presence of the results on UK websites?

I persevered, and found some interesting images and got some interesting results.

I started with a fairly standard image of a beach scene, always a favourite with testers. As you can see I got a pretty good first screen back. However, the 5th and 6th image on the top row show no sea or beach, neither do the first three images on the second row.

I moved on to an image of what looks like equipment at the top of a pole.

The results were much more mixed: studio shots of objects, fighting people, trucks etc. No images were returned that I would consider similar to the example picture.

Interesting results came from a similarity query on a clock face.  A couple of the first results hit the mark, then the results set degenerated into image similarity based more on the colour and the black background than anything else.

My last attempt, before morning coffee called, was an image of a country road. I was hoping that the clear roadway might produce a pretty precise results set. However, I was a little disappointed by what I saw.

The first results page only produced one vague road on the bottom row, with most of the similarity seemingly related to colours instead of objects.

From my less than scientific dip into this Google Labs offering, it looks like the highlighted images on the Google Similar Images home page produce good results - better results than I've seen other systems come up with. Many other image queries are sure to also produce results which may well impress. However, many of the results I saw did not match the initial level of accuracy I saw from the highlighted home page pictures.

I don't want to be picky, this is still a prototype after all, and well done to Google for introducing a wider audience to this type of image search. Hopefully, after more work, the results will increasingly make more sense to people, the access points offered to depicted content and conceptual aboutness will improve and more images will be more findable for more people.

Until that time, visual search without text will help with image findability, but text, metadata, and controlled vocabulary applied to images by people is for me still king, and will continue to offer the widest and deepest access to images for a long time to come.

Ian

Last month i asked the question "What is the Hardest Content to Classify?" and promised additional posts on the subject based on my background of 13 years developing taxonomy and indexing solutions for still images libraries, so I am continuing my thoughts in this post focusing on the basic attributes of image classification.

In my opinion, images are the hardest content items to classify, but luckily for sanities sake not all image classification is equally demanding.

The easiest elements of image classification relate to what I'm going to call image attributes metadata. This area, for me, covers all the metadata about the image files themselves, rather than information describing what is depicted in images and what images are about.

Metadata aspects in this area cover many things and there are also layers to consider:

1, The original object
-- This could a statue, an oil painting, a glass plate negative, a digital original, or a photographic print

2, The second generation images
-- The archive image taken of the original object, plus any further images, cut-down image files, screen sizes, thumbnails, images in different formats, Jpeg, Tiff etc

The first thing to think about is the need to create a fully and useful metadata scheme, capturing everything you need to know to support what you need to do. This may be to support archiving and/or search and retrieval.

Then look at what data you may already have or can obtain. Analyse data for accuracy and completeness and use whatever you can. Look to the new generation of digital cameras to obtain metadata from them. Ask image creators to create basic attribute data at the time of creation.

You'll be interested in the following metadata types:

- Scanner types
- Image processing activities
- Creator names
- Creator dates
- Last modified names
- Last modified dates
- Image sizes and formats
- Creator roles - photographers, artists, sculptures
- Locations of original objects
- Locations at which second generation images were created
- Unique image id numbers and batch numbers
- Secondary image codes that may come from various legacy systems
- Techniques used in the images - grain, blur etc
- Whether the images are part of a series and where they fit in that series
- The type of image - photographic print, glass plate negative, colour images, black and white images

This data really gives you a lot of background on the original and on the various second generation images created during production. Much of this data can either be obtained freely or cheaply, lots of it will be quick and easy to grab and enter into your systems. It should also be objective and easy to check.

My next post will cover dealing with depicted content in images. Please feel free to leave comments or questions on the subject.

Image|Flickr|Daniel Y. Go