Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System

The image features (local, global) pay vital role in image retrieval system. The effectiveness of these image features depends on the application domain, i.e., in some domains the global features generate better results while in others the local features give good results. Different species of fishes have different color, texture, and shape features in their body parts (head, abdomen, and tail). Previously most of the work, in fish image domain has been done using global features. This work claims that fish image retrieval system using local features can generate better results as compared to global features. This is because of the fact that fish image has different features in its body parts. In this research, a fish image segmentation algorithm is proposed to extract fish object from its background and then separate fish object into three distinguished body parts, i.e. head, abdomen, and tail. The proposed algorithm was tested on a subset of “QUT_fish_data” data set containing 369 fishes of various sizes of 30 species. The experimental results showed an accuracy of 87.5% on fish image segmentation and demonstrated the effectiveness of local features over global features.

@article{Baloch2017,
  title     = {Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {12},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Amanullah Baloch and Mushstaq Ali and Faqir Gul and Sadia Basir and Ibrar Afzal},
  doi       = {10.14569/IJACSA.2017.081252},
  url       = {https://doi.org/10.14569/IJACSA.2017.081252}
}