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How To Use ColdFusion to Search Images Based on Color

A starting point

This article describes a basic method for indexing and searching images and digital photographs based on color using ColdFusion and CFImageHistogram ( This method indexes and searches color in images quickly using ColdFusion.

I don't pretend to claim that this is the best way to search images based on color as there are other more in-depth and precise methods. For more information I suggest you read more about content-based image retrieval systems (

Note: The code examples below range from pseudo-code to 95% of what you will need to index and search images. There are so many possibilities of how images and image color information can be stored that it's hard to demonstrate a one-size-fits-all solution.

Where to Start
To be able to search for colors in an image we have to know something about the colors that make up that image. A histogram is a good way to do this. More important, a color histogram is useful because it tracks the frequency of colors that occur in an image. Each occurrence of a color, in this case red-green-blue, is counted based on its value 0-255.

To get the color histogram of an image, you need to inspect each pixel and calculate the red/green/blue components of that pixel. This can be a very slow process, so it may be best to resize a large image before trying to calculate the color histogram.

Some Statistics
The CFImageHistogram ( color histogram creates an array for each of the three colors (R-G-B). In each array there are 255 cells that store the count of the occurrences of that particular color (0-255). While the arrays are nice to have, it's more important to be able to search knowing the mean and standard deviation of that color's array. The mean will tell us what the average color is and the standard deviation will give us an idea of how spread out the color range might be.

Color Information
This statistical color information works well. We can take an image and find the average red, green, and blue for it, then store this information in a database and query for images that have a high occurrence of blue, for example. But the problem is that most images have a mix of colors, so when you retrieve the color histogram of the entire image, you get an average result that tends to be the average color for the entire image and that's most likely some grayish color (see Figure 1).

A solution to this is to split the image into sub-images. By looking at the color histogram information and the mean/standard deviation of smaller regions of the image, we can better search within that image. By looking in smaller areas of the image we're more likely to get average colors for the histogram that better reflect the colors in the image (see Figure 2).

The more sub-images (or bins) that you use, the more accurate your searches can be; however, there is a trade off in the processing time it takes to index the images and then later the search query time.

Indexing the Images
The code examples below might be a little unclear, so I've included a basic flow chart that should help in understanding the process of indexing the color information on the images (see Figure 3). This image is also available at

The first step to getting this color information out of an image is to buffer an image so we can manipulate it:

//image to get color information from
photo = expandPath("photo.jpg");
//Jpeg Codec
jpegCodec = createObject("java", "com.sun.image.codec.jpeg.JPEGCodec");
//file input
fileInputStream = createObject("java", "").init(photo);
//decodes the JPEG
decoder = jpegCodec.createJPEGDecoder(fileInputStream);
//get buffered image
bufferedImage = decoder.decodeAsBufferedImage();
imageHeight = bufferedImage.getHeight();//buffered image height
imageWidth = bufferedImage.getWidth();//buffered image width

Now that we have a buffered image, we need to be able to isolate the subimages within the image. The "subImage" method of the bufferedImage can be used. The subImage creates a new buffered image based on a rectangle defined in the arguments:

subImage = bufferedImage.getSubimage(x,y,width,height);

Once we have an isolated region of an image, we need to get the color histogram and color statistics of it. This can be done with the CFImageHistogram:

//create image histogram object
imageHistogram = createObject("component","imageHistogram").init();
//set the buffered subimage into the image histogram object
//get the color histogram and color statictics struct
hist = imageHistogram.getColorHistogram();

We need to store this image color histogram information in a database table so that we can query it. We'll also need to store a unique ID of the image (either a file name or database ID) and the means and standard deviations. CFImageHistogram will return a struct containing the histogram and color statistics for the R-G-B of the image. Here is an example of what that query might look Listing 1.

While looping through each of the 16 sub-images, we need to store this information based on a unique ID or the filename of this image so we can later query this data. Putting the above code snippets together we can index images. Listing 2 provides a more in-depth example of how to index a database or cfdirectory of images, storing color data for each sub-image within each image.

This process of storing the data of each sub-image needs to be repeated for all the images you would like to search. You can do this within a cfoutput of a cfdirectory or a query result of image locations. This process can be very slow; each image may take as long as 1-5 seconds to process depending on the size of the original image, the size and number of sub-images, and your server's speed. This can also create a large number of records, for example, 200 images at 16 bins per image = 3200 records.

Searching the Indexed Images
Once we have a database table populated with image region color information, we need to figure out how to search for a color. We'll do this based on a single color and look for images that have the most occurrences (or nearest) in their sub areas. We'll query the database looking for rows that are closest to the R-G-B color selected. We'll use a SQL "between".

That query will return all the matches of the colors that fall in those ranges, which is fine but we'd like to know which images have the most occurrences. We will add some grouping and sorting to the query so that we return, in order, the images that have the highest occurrence of the color selected (see Listing 3). Chances are fairly high that most selected colors won't return any exact matches to the selected color, so you may need to add more range that the record's color mean can fall into. You may even want to loop the query several times, each time expanding the range until you have, say, 10 results.

To view a demo color search (see Figure 4) go to:

Final Notes
This page article is meant as a starting point. There is much more that could be done and/or improved with this method. The search query could definitely be improved to better take into account the standard deviation of sub-images. Also since we are storing colors in areas of an image, it would be possible to search images based on patterns or drawings similar to the way retrievr ( works.

More Stories By Seth Duffey

Seth Duffey is the webmaster for the City of Davis, California. He is also the manager of the Sacramento ColdFusion Users Group. His blog is

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