Search Engine Optimization (SEO) for Photographers

Got your pictures on the web?  Disappointed with how many folks are finding you there?  Looking to build traffic to your web presence?  Perhaps a discussion on SEO (Search Engine Optimization) for photographers is in order.

SEO for Photographers

If you have a presence on the web, its there to be noticed. Getting noticed means having search engines find you. The web-o-sphere is full of advice on how best to structure your websites to make them friendly to the search engines. Some of the advice is real, most of it is redundant and a lot of is not focused on helping the photographer with a web site full of images.

If you are like me, then creating web sites and bending over backwards to make them search engine friendly is not your main interest in life. The trick is to find the key habits that help in the ranking process while not going too overboard. My ultimate game plan is to make the SEO  practices habitual so that as I load up content I make the right decision at the right time and minimize the amount of rework necessary while achieving reasonable to good results.

Why is SEO important?

While websites are full of pictures and the web is inherently a visual platform, search engines are incapable of seeing and appreciating your pictures. Search engines cannot distinguish a beautiful sunset and a bland one. They cannot tell the difference between a snapshot and a master photograph. Search engines index web pages and their content by looking at the codes that makeup the web page as well as the text on the web pages. What a picture shows has to be inferred from its surroundings on a web page rather than by direct view.

Fortunately search engines are designed by people and they operate predictably based on algorithms. But just how they work are closely guarded secrets to keep web page designers from unfairly tilting the scales. Ranking in the search results is a key business skill and for large companies can mean the difference between earning millions and going bust. Understanding, adapting to and tailoring web pages to make the optimal for search results is a big business.

So, if its so complex you must be wondering what you can possible learn here.

Fortunately in the whole mix there are plenty of niche markets to explore. Additionally a few simple changes to you habits and behaviors can pay off a lot. Measurable success can be had for following a few basic implementation patterns. Building up your audience can be done in small steps that culminate in a larger following.

Two kinds of SEO

SEO is generally divided into two categories; on-site and off-site. On-site SEO is is all the work your do to make sure your site is clean, fast loading and take full advantage of the sites structure to best represent your work. A lot of this is following simple and basic rules of design and making sure your hosting platform works for and not against you. Off-site SEO is what you do on the web on other sites to make yours more popular. A big part of SEO is understanding that the web is like high school. The more popular you are the more popular you will become. For your site to be ranked high, its important that other sites link to you and in doing so raise your importance. When other sites link to yours they are, in effect, voting for you. The search engines take note of how many votes you get and they rank popular sites higher then less popular sites.

We’ll look at both on-site and off-site SEO in upcoming articles.

How to Read A Histogram

The Histogram

Wondering how to read the histogram on the back of your camera? Not quite sure what highlights and mid tones are? Let’s take a walk through this basic tool used throughout the image capture and processing process.

While there are several ways to represent the color in an image, by far the most used form is to record each pixel or point as a combination of three values; a number for the red component, one for the green and one for the blue. The color of a pixel is a mixture of these three components and the intensity or brightness the scale of the values.

You often hear about “bit depth” in image files and camera sensors. This indicates the range of numbers for each color component. Eight-bit depth allows numbers for each component that are 0-255 in range. Twelve-bit depth allows numbers for each component in the range of 0 – 4095. The actual number of possible different colors when you combine the values from the three components is fairly large. For an image with an 8-bit depth the total possible combinations works out to about 16 Million colors; for the 12-bit depth, you get more than 68 Billion possible color combinations.


Proper Exposure

Getting the most from your camera means properly exposing the image with your sensor – but how do you know you are doing so? Proper exposure means not presenting too much light which will overwhelm the sensor or too little light which will not provide enough energy for proper recording. Providing this insight is where the histogram becomes useful.

Given the bit depth of the sensor, each pixel can take on only certain values. A histogram is a tool that shows you how many pixels in the image are recorded at what value. For historical reasons histograms are expressed in an 8-bit range – numbers 0 to 255. Each point or bin in the histogram represents how many pixels in the image have a particular value. The #10 bin represents how many pixel values had the value 10, the #20 bin is similarly the number of pixels that have the value 20.

For sensors or files with more than an 8-bit depth, the histogram represents a scaled version from the smallest to the largest value. Histograms are often done multiple ways – you can have an individual histogram for each color component or a single histogram representing the combined energy in the pixel; these are called luminance histograms.

So how do you read a histogram?

A well-recorded image is one where you have numeric values spread across the range of possible values. Below is an example of an under-exposed, over-exposed and properly exposed image. In the over-exposed image the histogram appears to be chopped off at the top. This tells you that the image is too bright and that the sensor had to limit or clip the image causing loss of information. Similarly, an underexposed information means the scene was too dark for you to record values and again you have lost information.

Highlights, Mid-Tones and Darks.

In discussing image processing terms like “lights”, “darks” or “mod tones” are often used. These are simple references to the ranges of light in the image and correspond to different parts of the histogram.