Biodiversity image retrieval framework for monogeneans

Abu, Arpah; Lim, Susan L.‐H.; Sidhu, Amandeep S.; Dhillon, Sarinder Kaur

doi:10.1080/14772000.2012.761655

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…Integrating fish species data from all the available sources helps us explore new relationships such as host-parasite relationship among fish and monogeneans discussed briefly in these papers (Abu et al, 2013a(Abu et al, , 2013b. These relationships that are modelled in the digital biological ecosystem and shown visually by GIS can be further explored by marine scientists in their research.…”

Section: Discussionmentioning

confidence: 96%

“…Through the proposed system we intend to provide integrated data and information in an easily understandable format. This data integration effort also will assist researchers to explore relationships in biological ecosystems as they are modelled in the digital counterpart (for instance, the relationships between fish and their monogenean parasites that we recently published; Abu et al, 2013aAbu et al, , 2013b.…”

Section: Introductionmentioning

confidence: 98%

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A model of a digital biological ecosystem

Dhillon

Chiew

Leow

et al. 2013

Systematics and Biodiversity

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In this paper, we propose a model of a digital biological ecosystem of fish. Collections of fish data by scientists in Malaysia have been converted and developed into a digital biological ecosystem using computational technology and methods. We used a combination of Geographical Information System, databases and ontologies for storing, retrieving and visualizing fish species distribution and their abundance as well as their relationship with the environment. We used ArcGIS to build the fish and environmental geodatabase, which is linked to Google Earth for real-time visualization, and Surfer 8 to create contour maps which are superimposed as layers on the ArcGIS interface. Individual fish species on the map are hyperlinked to the FishBase Portal (http://www.fishbase.org/) while the published map is displayed using ArcReader. The three databases that we created contain information about fish species, environment and contours. These are also integrated using a fish ontology which will link all the available information for various fish species. The proposed system can be adopted by marine scientists to better convince economists, fishery managers, coastal developers and government bodies who make important decisions on conservation policies and laws.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

A model of a digital biological ecosystem

Dhillon

Chiew

Leow

et al. 2013

Systematics and Biodiversity

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“…We have used this ontology to improve the efficiency of CBIR for Biodiversity [20,21]. As a result the relevancy rate of results provided by CBIR increases due to the decrease in the size of the training set as most the images are relevant to the query image.…”

Section: Discussionmentioning

confidence: 99%

Semantic representation of monogenean haptoral Bar image annotation

et al. 2013

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BackgroundDigitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.ResultsIn this paper, we utilized the Taxonomic Data Working Group Life Sciences Identifier (TDWG LSID) vocabulary to represent our data and defined a new vocabulary which is specific for annotating monogenean haptoral bar images to develop the MHBI ontology and a merged MHBI-Fish ontologies. These ontologies are successfully evaluated using five criteria which are clarity, coherence, extendibility, ontology commitment and encoding bias.ConclusionsIn this paper, we show that unstructured data can be represented in a structured form using semantics. In the process, we have come up with a new vocabulary for annotating the monogenean images with textual information. The proposed monogenean image ontology will form the basis of a monogenean knowledge base to assist researchers in retrieving information for their analysis.

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“…Table 1 demonstrates some databases used in automated identification systems. Abu et al [1] proposed an image retrieval framework for monogeneans that contains two databases, the monogenean image database and the MHBI-Fish ontologies. An ontology framework improves the relevancy of the training set to collect the most relevant images to be used.…”

Section: From Image Acquisition To Databasementioning

confidence: 99%

How automated image analysis techniques help scientists in species identification and classification?

2018

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Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre-ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef-forts on identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179-193).

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Biodiversity image retrieval framework for monogeneans

Cited by 11 publications

References 31 publications

A model of a digital biological ecosystem

A model of a digital biological ecosystem

Semantic representation of monogenean haptoral Bar image annotation

How automated image analysis techniques help scientists in species identification and classification?

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