GrabCut

GrabCut is a classical algorithm of foreground extraction with minimal user interaction. It takes an input image and a user-defined bounding box that encloses the foreground object as its input (here dog is the foreground object). It then generates a refined segmentation mask that separates the foreground object from the background.  

With an initial estimate of foreground and background regions based on the provided bounding box a Gaussian Mixture Model (GMM) is used to model the foreground and background by iteratively updating the pixel labels, improving the accuracy of the segmentation. The final output of the GrabCut algorithm is a mask image where the foreground and background regions are separated.

Why use GrabCut and Mask R-CNN together for Image Segmentation?

Now a question arises, Why are we using GrabCut with Mask R-CNN, isn’t Mask R-CNN sufficient for image segmentation?

While Mask R-CNN is capable of producing reasonably accurate segmentation masks, there are instances where the results may contain noise or inaccuracies. This could be due to factors such as complex backgrounds, occlusions, or ambiguous object boundaries. By integrating GrabCut with Mask R-CNN, the segmentation masks can be further refined, resulting in more accurate and precise object boundaries. 

Also, Combining Mask R-CNN and GrabCut can offer a more robust and accurate segmentation result. Mask R-CNN excels at automatically predicting bounding boxes and segmentation masks but may introduce subtle background artifacts. GrabCut, though requiring manual input, is effective in precise segmentation but may struggle with automation.

By using them together, you can leverage the automation of Mask R-CNN for initial segmentation and then refine it with GrabCut, benefiting from the strengths of both methods to achieve a cleaner and more accurate segmentation of foreground objects from the background. This hybrid approach balances automation and precision in image segmentation tasks.

Image segmentation plays a crucial role in computer vision tasks, enabling machines to understand and analyze visual content at a pixel level. It involves dividing an image into distinct regions or objects, facilitating object recognition, tracking, and scene understanding. In this article, we explore three popular image segmentation techniques: Mask R-CNN, GrabCut, and OpenCV.

Let’s understand, What Image Segmentation with Mask R-CNN and GrabCut are?

What is R-CNN?

R-CNN stands for Region-based Convolutional Neural Network. It is a ground-breaking object detection system that combines object localization and recognition into an end-to-end deep learning framework. 

RNN can be summarised in the following ways.

• Region Proposal: Initially, a region proposal algorithm (such as selective search) generates a set of potential bounding box regions in an image that are likely to contain objects of interest. These regions serve as candidate object locations.
• Feature Extraction: Each region proposal is then individually cropped and resized to a fixed size and passed through a pre-trained CNN (such as AlexNet or VGGNet). The CNN extracts high-level features from the region, transforming it into a fixed-length feature vector.
• Classification and Localization: The feature vector obtained from the CNN is fed into separate fully connected layers. The classification layer predicts the probability of different object classes present in the region, while the regression layer refines the coordinates of the bounding box, improving localization accuracy.
• Non-Maximum Suppression (NMS): To eliminate redundant detections, non-maximum suppression is applied. It removes overlapping bounding boxes, keeping only the most confident detection for each object instance.