Cluster-Based One Shot Deeplabv3+ for Bird Image Segmentation

In this work, clustering based one shot approach for segmentation of bird images from complex background using Deeplabv3+ is proposed. Hierarchical clustering technique employed with average linkage is used to obtain clusters of bird images consisting of a particular class. The concept of clustering is exploited to capture intra-class variations and to train the learning model more efficiently and more effectively. Mobilenetv2 architecture is explored to extract features from each bird image, which are used for clustering. After obtaining clusters for each bird's species (class), one representative bird image from each cluster is considered for training the model. The remaining samples in the cluster are used for testing the model's performance in segmenting the bird image from complex background accurately. The proposed segmentation approach is tested on the benchmark bird image dataset called CUB-200-2011 dataset. The experimental results have proved that the proposed cluster-based one shot Deeplabv3+ approach has produced good results with less training.