Name
Title | ||
|---|---|---|
Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IJCNN.2013.6707084 | Neural Networks |
Keywords | Field | DocType |
ecology,image classification,image colour analysis,marine engineering,object recognition,support vector machines,CQ-HMAX biologically inspired color approach,HMAX model,SIFT,benthic organisms,classification,coastal defense,coastal environments,color information,computational models,coral reef ecosystems,exceptional biodiversity,grayscale shape information,high level inferotemporal cortex,image dataset,marine organisms,object recognition,primate visual system,scene recognition,support vectors machines,tropical zones,underwater images | Biodiversity,Population,Pattern recognition,Computer science,Support vector machine,Benthic zone,Computational model,Artificial intelligence,Contextual image classification,Grayscale,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4673-6128-6 | 2 |
PageRank | References | Authors |
0.40 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sepehr Jalali | 1 | 7 | 1.66 |
| Paul J. Seekings | 2 | 5 | 1.23 |
| Cheston Tan | 3 | 155 | 15.27 |
| Hazel Z. W. Tan | 4 | 2 | 0.40 |
| Joo-Hwee Lim | 5 | 783 | 82.45 |
| Elizabeth A. Taylor | 6 | 48 | 5.80 |