shops and building recognition application [closed]
I read about Bag of Visual words strategy used in parallel with SVM classifier , I wonder if this strategy will work with my system shops and building recognition , I think BOW strategy will not work gracefully because the objects are nearly same in structure is'nt it ? . I need some experienced people to guide me to the right way to do such a system before i go in the wrong way . Is there a better strategy than BOW for recognition from that type ? Some of my Datasets . It consist from 3 large building and 23 shops.
As you can see ,There are a few points to discriminate in my datasets and there's more 20 object .
It all depends on how your objects look like and if can you extract meaningful local features if BoW is useful or not. If you have structureless objects, then maybe just a contour comparison will do better. Maybe you can add some images of your objects to your post.
ok , great Idea
btw, this one also has a nice kmajority solution, in case you want binary features, like akaze or brisk
@Abu Gaseem: your problem looks like it should be good doable by means of BoW
@berak: cool, thanks for the link!
never feel sorry for asking the why question here !
let me try a 'diplomatic' approach: if you got 25000 images, you'll sincerely gain from the BOW reduction, if you only got 20, - get more data !
(usually with machine learning, you gain a lot with collecting a lot of data, and then cutting it down to the relevant parts, BOW is just another means in this direction)
the flann/brute-force approach is nice, if you only have sparse data / few images to compare
Yes you are right berak , I was checking the SIFT & SURF with two matcher strategy , Brute Force and FlannBased , I gain good result (inliers) when the two Images are for the same object in my data-sets , in contrast there was an negative inliers in other pairs of Images which are'nt for the same object , the reason for that my data-sets has high texture which is the stone of the buildings as you can see above I posted some training Images also they are too bad , I'm planning to take a new dataset without outliers as possible as i can , I think the BOW will be better on my dataset because you know there's a dictionary of words/features and Features are reduced as you mention , the problem is we cannot predicate how much the produced dictionary are distinctive. I will take with your advics
also SIFT has high repeatability with my data-sets. Thanks very much @berak ,@Guanta,
@berak,@Guanta I think you misunderstand the situation .,when you ask me to increase the data-set more than 20 Image. the 23 (3 buildings + 20 shops) is not the number of my data-set/training Images ,but it's the number of classes, each building and shops has a label as you can see the third Image above the system should response with Nour label . I dont think the BOW will be the right choice . I hope my note reach to you guys .
I think @berak and me understood you quite well, and still: BoW is one way you could solve this problem.
ok I will try the BOW . I wonder if you can guide me with how i should capture POI Images ,in context of make zoom in, in order to get rid of the outliers , angle of capturing (i.e variants view for the same POI ) ,and the resolution .my phone can capture photos at min-mum quality 480X640 pixels.