Results

As we can observe that when all the similarities are used in combination, It gives us best MABO. However, it can also be conclude RGB is not best color scheme to use in this method. HSV, Lab and rgI all performs better than RGB, this is because these are not sensitive to shadows and brightness changes. 

But when we diversify and combine these different similarities, color scheme and threshold values (k),

In selective search paper, it applies greedy method based on MABO on different strategies to get above results. We can say that this method of combining different strategies although gives better MABO, but the run time also increases considerably.

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.