Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2013.6707084
Title: Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach
Authors: Jalali, S.
Seekings, P.J. 
Tan, C.
Tan, H.Z.W.
Lim, J.-H.
Taylor, E.A.
Issue Date: 2013
Source: Jalali, S.,Seekings, P.J.,Tan, C.,Tan, H.Z.W.,Lim, J.-H.,Taylor, E.A. (2013). Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach. Proceedings of the International Joint Conference on Neural Networks : -. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2013.6707084
Abstract: In many coastal environments, particularly in tropical zones, coral reef ecosystems have exceptional biodiversity, contribute to coastal defense, provide unique and important habitats and valuable commercial resources. Assessment of environmental impacts on biodiversity in such areas are increasingly important to mitigate potential adverse effects on specific ecosystems. Visual classification of marine organisms is necessary for population estimates of individual species of corals or other benthic organisms. In this paper, we introduce a new image dataset of benthic organisms that are of different colors, shapes, scales, visibility and are taken from different viewpoints. We evaluate several different classification approaches on this dataset, and show that CQ-HMAX, our new biologically inspired approach to utilizing color information for object and scene recognition, that is inspired by the characteristics of color- and object-selective neurons in the high-level inferotemporal (IT) cortex of the primate visual system, results in better classification results in comparison with existing computational models such as support vectors machines, SIFT based approaches and the HMAX biologically inspired approach. We show that concatenating our model which encodes color information with the HMAX model which encodes grayscale shape information results in the highest classification accuracy. © 2013 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/116052
ISBN: 9781467361293
DOI: 10.1109/IJCNN.2013.6707084
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