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

Abstract

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.

Authors and Affiliations

Amanullah Baloch, Mushstaq Ali, Faqir Gul, Sadia Basir, Ibrar Afzal

Keywords

Related Articles

Improved-Node-Probability Method for Decision Making in Priority Determination of Village Development Proposed Program

This research proposes a new method, the probability of nodes (NP) and the cumulative frequency of indicators within the framework of Bayesian networks to calculate the weight of participation. This method uses the PLS-P...

An Efficient Fault Tolerance Technique for Through-Silicon-Vias in 3-D ICs

Three-dimensional integrated circuits (3D-ICs) based on Through-Silicon-Vias (TSVs) interconnection technology appeared as a viable solution to overcome problems of cost, reliability, yield and stacking area. In order to...

Towards a Context-Dependent Approach for Evaluating Data Quality Cost

Data-related expertise is a central and determining factor in the success of many organizations. Big Tech companies have developed an operational environment that extracts benefit from collected data to increase the effi...

Analysis of Heart Rate Variability by Applying Nonlinear Methods with Different Approaches for Graphical Representation of Results

There is an open discussion over nonlinear properties of the Heart Rate Variability (HRV) which takes place in most scientific studies nowadays. The HRV analysis is a non-invasive and effective tool that manages to refle...

Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to a...

Download PDF file
  • EP ID EP259573
  • DOI 10.14569/IJACSA.2017.081252
  • Views 93
  • Downloads 0

How To Cite

Amanullah Baloch, Mushstaq Ali, Faqir Gul, Sadia Basir, Ibrar Afzal (2017). Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System. International Journal of Advanced Computer Science & Applications, 8(12), 396-403. https://europub.co.uk/articles/-A-259573