I have a problem. I have six pieces of work to write in a couple of weeks and I'm under pressure. I need the work to be spot on, of the highest quality and created in the shortest space of time.

The answer to my problem? Buy a new computer.

Does this sound strange to you? Can you see how improved output comes from a new computer?

I was sceptical, but the Sales guy said a new computer was the answer. I asked him to explain and he told me how the time I was wasting messing with my old computer was at the heart of my problem. All those lost minutes fixing crashes, worrying about blue screens, battling with slow performance, scanning for adware, spyware and worse. Forget all that was the message I was getting, move to the promised land of a newer, faster computer and your problems are solved. After a bit more chat I was sold. My new computer would save me time and that extra time would be spent devoted to my key tasks, which in turn would lead to better quality work and faster work at that. Saving time was even money in the bank for me to set against the cost of the computer - so it wasn't even as expensive as I'd thought.

At this point I excused myself, had a coffee, and thought it through one more time. Did it make sense that a new computer was my solution? The light quickly dawned, of course it didn't. A new computer wasn't the solution and time saving was not my key issue. How did the Sales guy know that time saved would be time I'd actually spend on my document tasks? How did he know the processes and tasks I'd been performing with my current computer were not valuable experiences - not to be lightly ignored. Why did he make no attempt to understand me and my circumstances and simply sell me the one size fits all Sales line that so many people still hear today?

I soon realised than I'm better off assessing my goals and objectives. What is it I need to do? For whom? Why? And when? Then I need to ensure I'm prepared and enabled to achieve them. Is my broadband connection operating? Is it fast enough? Is the right software up and running? Can I access the libraries I need?

I would also benefit from improving my time planning and management skills. I need to focus on my key tasks. What is it I need to do? What problems am I having here? I also should not forget my deliverables. What do I need to produce and how do I get there?

All these areas, when addressed in the right way, will enable my tasks and improve my outcomes. Granted, this is a little harder to sell than a new computer equals better work and a wonderful life, but surely I'm worth that extra effort and it's certainly what I need to hear.

Many of us encounter this scenario frequently. How many times have you watched a Sales presentation built around saving time? Usually a calculator is involved and sometimes members of the audience are asked to volunteer key pieces of information - "How much time do you spend searching for information in a day?", "What's your hourly rate?", "How hard do you find tracking down the information you need?" "Could you be more productive if you saved some of this time?" Very often 'time saved' is then calculated and that 'time saved' directly equated to business advantage. Very often there is little or no thought put into the needs or objectives of individual businesses or any injection of common sense into the Sales pitch.

A Dow Jones information assessment looks for the real issues and pain points our clients experience, and works with them to solve their problems and enable improved outcomes. If you have an information management issue you need assistance with, speak to us and let us work with you to get to the heart of your needs. You never know you might even save enough money to afford that new computer you've always wanted!

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’.

If you have been keeping up with my posts on this blog you won't be surprised to learn that today I spent my lunch hour exploring a video search offering that's new to me called VideoSurf. I was so interested in this new search tool that I interrupted my usual run of image indexing articles, and my lunch hour, to do some research and write up this post.

In a September press release VideoSurf claimed its computers can now, "see inside videos to understand and analyze the content." I would encourage anyone who has an interest in this area to take a look at the company's website, give it a whirl and see what they think.

In my experiences video search engines have relied on a combination of the metadata that is linked to the video clips, scene and key frame analysis, and automatic indexing of sound tracks synched with the video.

For example, sound tracks, synchronised to video content, can be transformed to text and indexed and then can be linked to sections of videos by looking for gaps in the video to identify scenes, with various techniques also used to create key frames, that attempt to represent a scene. These techniques are backed up with metadata to accompany a video clip.

If you have worked in the industry you know that video metadata is expensive to create. Most of what people see online is either harvested for free from other sources, or limited in size and scope. Such metadata may cover the title of a video clip, text describing the clip, clip length .etc. It may even include some information about the depicted content in the video or even abstract concepts which try to specify what a clip is about. Though this level of video metadata is the most time consuming and complex to create - it also offers the fullest level of access for users.

Audio tracks can be also be of great use and many information needs can be met by searching on audio in a video. There are however limitations; for example many VERY SCARY scenes have little dialogue in them, and depend heavily on camera-work and music to give the feeling of fear, how easy is it to find these scenes based on dialogue alone, or even based on 'seeing inside a video'. How can you look for 'fear' as a concept?

Content based image retrieval, looking at textures, basic shapes, and colours in still images, has yet to offer the promised revolution in image indexing and retrieval. In some contexts it works quite well, in many contexts end-users don't really see how it works at all. So adding a layer to video search that tries to analyse the actual content, pixel for pixel is an interesting development.

To my mind, a full set of access paths to all the layers of a video still demands the use of fairly extensive metadata, especially for depicted content and abstract concepts. Up to now, metadata has always been the way to find what an image, whether it's still or moving, is conceptually about, and what can be seen in individual images and videos. Even when that metadata is actually sounds, turned into text and stored in a database.

Is VideoSurf's offering really any different from what's gone before?

Is this system, which seems to be using Content-Based Image Retrieval (CBIR technology to some extent, a significant advance?

Reviewing some of the blog posts people have published it seems many others are interested in VideoSurf's offering as well.

For an initial idea as to how VideoSurf works, try taking a look at James McQuivey's OmniVideo blog post, "Video search, are we there yet?-. As James describes in the article, one pretty neat aspect of what VideoSurf can do is to match faces, enabling you to look for the same face in different videos, thus reducing the need to have the depicted person mentioned in the metadata exclusively. However, this clearly isn't much help if the person you're looking for is mentioned but not depicted, in which case indexed audio would help, or if the person is not well depicted, for example the person is only depicted from the side or the back. However, quibbles aside, if this works, then this is a pretty useful function in itself.

Here are some of the other bloggers who have be writing their thoughts on Video Surf. For example:

• An interesting post on this subject from the Rhondda's Reflections blog on Searching for videos with VideoSurf
• Phil Bradley comments on his Weblog on the VideoSurf Video Search
• And one of the the best current reviews of VideoSurf that I've found comes from Chris Sherman at SearchEngineLand.

Clearly, we're on the right track and there is a lot of interest in the opportunities and technologies around video search. However I think that there is a long way to go before detailed and automatic object recognition is of any meaningful use to people. As far as I can see, it's still not there with still or moving digital images. Metadata for me is still the 'king' of visual search. There however are a growing number of needs that automatic solutions can already resolve and a growing case for solutions that work by offering a combination of automatic computer recognition of image elements, metadata schemes and controlled vocabulary search and browse support.

I'd love to know what people think, about VideoSurf and other services that provide video search.