I’m working on a software project in which I have to compare a set of ‘input’ images against another ‘source’ set of images and find out if there is a match between any of them. The source images cannot be edited/modified in any way; the input images can be scaled/cropped in order to find a match. The images can be in BMP,JPEG,GIF,PNG,TIFF of any dimensions.
A constraint: I’m not allowed to use any external libraries. ImageMagick is an exception and can be used.
I intend to use Java/Python. The software is purely command-line based.
I was reading on SO and some common image comparing algorithms. I’m planning to take 2 approaches.
1. I could use Histograms/buckets to find out the RGB values of the 2 images being compared.
2. Use SIFT/SURF to fin keypoint descriptors and find the euclidean distance between them and output the result based on the resultant distance.
The 2 images in comparison can be in different formats. An intuitive thought is that before analysis/comparison, the 2 images must be converted to a common format.I reasoned that the image should be converted to the one with lesser quality e.g. if the 2 input images are BMP and JPEG, convert the BMP to JPEG. This can be thought of as a pre-processing step.
My question:
Is image conversion to a common format required? Can 2 images of different formats be compared? IF they have to be converted before comparison, is my assumption of comparing from higher quality(BMP) to lower(JPEG) correct? It’d also be helpful if someone can suggest some algorithms for image conversion.
EDIT
A match is said to be found if the pattern image is found in the source image.
Say for example the source image consists of a football field with one player. If the pattern image contains the player EXACTLY as he is in the source image, then its a match.
No, conversion to a common format on disk is not required, and likely not helpful. If you extract feature descriptors from an image (SIFT/SURF, for example), it matters much less how the original images were stored on disk. The feature descriptors should be invariant to small compression artifacts.
A bit more…
Suppose you have a BMP that is an image of object X in your source dataset.
Then, in your input/query dataset, you have another image of object X, but it has been saved as a JPEG.
You have no idea how what noise was introduced in the encoding process that produced either of these images. There is lighting differences, atmospheric effects, lens effects, sensor noise, tone-mapping, gammut-mapping. Some of these vary from image to image, others vary from camera to camera. All this is done before the image even gets saved to storage in the camera. Yes, there are also JPEG compression artifacts, but to assume the BMP is “higher” quality and then degrade it through JPEG compression will not help. Perhaps the BMP has even gone through JPEG compression before being saved as a BMP.