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Here some answers to the rumors you 'heard':

• Yes, you need to be careful when using SIFT and SURF commercially.
• About detectors, descriptors and matchers:

1. There exist keypoint detectors which detect locations in the image containing high entropy or with a certain criteria (like corners, blobs). Often they also encode scale and rotation information which make then a feature invariant to those transformation (if it can make use of it at all). A list of possible keypoint-detectors can be found at http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html#featuredetector-create

2. Then there exist feature (or descriptor) extractors which typically use the keypoint locations to build a descriptor. There exist two categories of features, binary ones (FREAK, BRISK, ORB, BRIEF) and non-binary ones (SIFT, SURF, MSER). Binary features are typically faster to compute and offer a more compact representation. They are often used in robotics for a fast match. In the case of image retrieval the most common choice is SIFT. A list of supported Features can be found here: http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_extractors.html#descriptorextractor-create Typically you can combine any possible keypoint detector and feature extractor of OpenCV (apart from the SIFT detector). However, not each combination makes sense or works as expected. Natural combinations or combinations suggested by the authors of the descriptors are afaik SIFT-SIFT, ORB-ORB, SURF-SURF, BRISK-BRISK, BRISK-FREAK, MSER-MSER, FAST-BRIEF.

3. For the matchers you have the choice if you want to use the brute-force matcher or the flann based matcher. The latter one is preferable if you have a very large database of features. It creates an index of all features which is then queried. The brute-force matcher in contrast just goes over all features in question and picks the best match. It depends on the dataset and the task you have what you should use (typically BFMatcher is fine). Furthermore you need to take care that if you use a binary feature you should compare them with the Hamming-norm not with the L2 norm.

• It's not about keypoints rather descriptors. You can find more information about bag of words here: http://answers.opencv.org/question/8677/image-comparison-with-a-database/#8686
• Classifiers are used to distinguish between classes, e.g. dogs and cats. The information about a class has to be learned, i.e. a classifier needs to be trained for that.

Good luck with your project!