1
$\begingroup$

I've been reading a lot about computer vision lately, and while there is a huge amount of info about object classification, and a lot less on object detection, I have not found anything on class-based object detection i.e when I know what I am looking for. For example, looking for cats on a picture. Nowadays you can say if a picture is a cat (object classification), or if the picture has cats among other things. How would I use this knowledge to improve the performance (in both accuracy and speed)?

Somethings that I wouldn't do (but i'm tempted to do):

  1. Perform a brute-force solution with a binary classifier
  2. Implement a shape-based selective search and then use a binary classifier

What i would really like to do, but haven't found in the literacy:

  1. Implement a trainable selective search to find class-based regions of interest.
  2. Use a binary classifier to check if ROI is in fact the object to be found.
$\endgroup$
2
$\begingroup$

Object detection models (such as SSD, Faster-RCNN, YOLO, R-FCN) are trained to detect specific classes.

If you wish to detect a single class, you could train a custom model on this class.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.