Selective Search In Object Recognition

The result generated on VOC 2007 test set is,

As we can see that it produces a very high recall and best MABO on VOC 2007 test Set and it requires much less number of windows to be processed as compared to other algorithms who achieve similar recall and MABO.

Applications :

Selective Search is widely used in early state-of-the-art architecture such as R-CNN, Fast R-CNN etc. However, Due to number of windows it processed, it takes anywhere from 1.8 to 3.7 seconds (Selective Search Fast) to generate region proposal which is not good enough for a real-time object detection system. 

Reference:

• Selective Search paper (Selective Search for Object Detection)
• Stanford Computer Vision Slides

The problem of object localization is the most difficult part of object detection. One approach is that we use sliding window of different size to locate objects in the image. This approach is called Exhaustive search. This approach is computationally very expensive as we need to search for object in thousands of windows even for small image size. Some optimization has been done such as taking window sizes in different ratios (instead of increasing it by some pixels). But even after this due to number of windows it is not very efficient. This article looks into selective search algorithm which uses both Exhaustive search and segmentation (a method to separate objects of different shapes in the image by assigning them different colors).

Algorithm Of Selective Search :

1. Generate initial sub-segmentation of input image using the method describe by Felzenszwalb et al in his paper “Efficient Graph-Based Image Segmentation “.
2. Recursively combine the smaller similar regions into larger ones. We use Greedy algorithm to combine similar regions to make larger regions. The algorithm is written below.

   Greedy Algorithm : 
   
   1. From set of regions, choose two that are most similar.
   2. Combine them into a single, larger region.
   3. Repeat the above steps for multiple iterations.

    

3. Use the segmented region proposals to generate candidate object locations.