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Fractal adaptive weight synthesized–local directional pattern–based image classification using enhanced tree seed algorithm

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Abstract

Coral reefs are one of the most prominent marine ecosystems on the Earth; they are threatened due to various factors, including growing anthropogenic impacts and the effects of global change. Hence, the automatic classification of coral species is significant for tracking and detecting threatened and susceptible coral species. This paper proposes a new feature descriptor known as fractal adaptive weight synthesized–local directional pattern (FAWS-LDP) method to classify coral images using enhanced tree seed algorithm with extreme learning machine (ETSA-ELM) technique. The proposed feature descriptor (FAWS-LDP) inherits the advantage of both fractal pixel intensity information and local directional characteristics by indexing both feature vector values. Finally, the extracted features are imported to the extreme learning machine (ELM) network for classification. The ELM classifier is a single hidden layer feed-forward neural network with a faster learning speed and produces good generalization performance. The random selection of input weight and biases of the ELM classifier produces non-optimal or unnecessary input biases and weights to the network. Hence, to fine-tune the parameters of the ELM classifier, an enhanced tree seed algorithm (ETSA) is proposed. The proposed ETSA is a new learning technique to overcome the drawbacks like local optima and a lower coverage rate. The classification performance of ELM employing the ETSA optimizer is compared to the original TSA and other well-known metaheuristic algorithm (MHA) trainers, such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), utilizing model performance measures like specificity, sensitivity, and classification accuracy. The evaluation of coral classification datasets reveals that the proposed ETSA-ELM produces consistently superior performance to existing methods. Finally, the proposed feature descriptor technique is statistically analyzed using a non-parametric Friedman test to demonstrate the efficiency.

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Authors and Affiliations

  1. Underwater Acoustic Research Lab, Department of Electronics and Communications Engineering, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai, Kalavakkam, Chennai, 603110, India

    Annalakshmi Ganesan & Sakthivel Murugan Santhanam

Authors
  1. Annalakshmi Ganesan
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  2. Sakthivel Murugan Santhanam
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Contributions

Annalakshmi Ganesan and Sakthivel Murugan Santhanam contributed to the methodology, validation, data analysis, performance evaluation, manuscript drafting, and editing.

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Correspondence to Annalakshmi Ganesan.

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Appendix

Appendix

The detailed description about the various dataset used in this experiment is presented as follows.

  • •RSMAS dataset

  • The RSMAS dataset was created using reef survey images taken by divers from the Rosenstiel School of Marine and Atmospheric Sciences, Miami. This dataset contains images of many types of underwater coral reefs obtained with various cameras at various times and locations. The database comprises 766 picture patches of 14 different classes, with a 256 × 256 pixel image size.

  • •MLC dataset

  • The Moorea Labeled Corals (MLC) dataset is a sample of images acquired from the Moorea Coral Reef Long-Term Ecological Research site (MCR-LTER) for computer research. It includes 2055 images from three environments: fringing reef, outer 10 m, and outer 17 m taken between 2008 and 2010. It also contains random points that have been annotated. This includes 24,159 images with nine different classes.

  • •EILAT dataset

  • The EILAT dataset images were acquired Eilat in the Red sea. It has a total of 1123 image patches and each of which is 64 × 64 pixels in size. The images were visually sorted into eight classes by the experts. In comparison to the others, the two classes have a considerable number of samples. These image patches were created using the same camera that captured the original, full-size photographs.

  • •EILAT2 dataset

  • This EILAT2 dataset is created by the acquired image from the Red sea. Totally, this has 303 image patches. A group of professionals visually segregated the images into five categories: urchin, branching coral, sand, favia coral, and brain coral. The batch size for images is 128 × 128. All of the images in this database were captured using the same camera.

  • •KTH-TIPS dataset

  • The Kungliga Tekniska Hogskolan Textures under changing illumination, position, and scale (KTH-TIPS) dataset includes images of ten different types of natural materials with varying scale, illumination, and pose. The images were acquired at nine distinct scales with two-octave spanning. There are 810 images in all, divided into ten different classes.

  • •Brodatz dataset

  • Brodatz is a popular and all-purpose dataset that includes natural textures. The Brodatz dataset contains 112 texture images with a resolution of 640 × 640 pixels. The Brodatz album includes various constraints, such as a single illumination and viewing direction for each texture. It has 112 different texture classes, each of which is represented by a single image separated into nine subimages.

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Ganesan, A., Santhanam, S.M. Fractal adaptive weight synthesized–local directional pattern–based image classification using enhanced tree seed algorithm. Environ Sci Pollut Res 29, 77462–77481 (2022). https://doi.org/10.1007/s11356-022-20265-3

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