Image Processing Reference
In what regards the local descriptors, probably the most famous algorithm (scale invariant
developed. Some of the most popular ones are based on speeded up robust feature (SURF)
3 Our approach
The proposed approach targets to classify a mixed set of images, containing real world scenes
and document scans. The system mainly follows the standard CBIR architecture as it can be
FIGURE 1 The basic system architecture.
• the training and learning module;
• the document classification module.
A valid use case scenario contains the below stages:
• the system is trained on a set of images;
• each image is analyzed and decomposed in relevant descriptors;
• the descriptors are provided as input to a machine learning module, which is in charge of
setting the class boundaries;
• each new regular image (not document) is decomposed and classified accordingly;
• each new document scan is preprocessed and segmented. The extracted images are then
• the system extracts the 10 most relevant results and provides them as an answer to the user
The indexing process is based on supervised machine learning and is conducted on regular
images. The user is allowed to enter queries based on both image types.
We are using a mixed set of image characteristics:
• different color spaces;
• texture space;
• local descriptors.
We have not used any shape descriptors, as the preliminary tests showed that in this area
these do not produce a noticeable improvement. The main problem was caused by the fact
that the objects contained in the images may be affected by problems like occlusion or clutter.
In the color descriptors area, we have used four sets of characteristics, as it follows:
• c1c2c3 and l1l2l3 . As explained above, these color spaces are very useful when applied on
real world images. The coordinates are described by the equations below: