ONTOLOGY BASED KNOWLEDGE EXTRACTION FROM FRUIT IMAGES

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1 International Journal of Computer Engineering and Applications, Volume X, Issue IV, April 16 ISSN ONTOLOGY BASED KNOWLEDGE EXTRACTION FROM FRUIT IMAGES Department of Computer Applications, SD College Hoshiarpur, India ABSTRACT: Ontology is branch of artificial intelligence which help to represent domain knowledge, concepts and semantic relationship among them. Ontologies can be used to represent details about fruits by first classifying the fruit in image. Keywords: Ontology, fruit images classification, knowledge extraction from image, image understanding [1.1] INTRODUCTION As large numbers of images are available on internet, searching image that contains desired items can be a cumbersome task. Mostly images are described with manual keywords. But attaching keywords manually to describe an image can be misleading sometimes.e.g if an image containing of strawberry is manually described with STRAWBERRY and user searches with keyword Fragaria X ananassa then that image might not be retrieved, although, Fragaria X ananassa is a scientific name of Strawberry..so there is a need of building taxonomy of knowledge present in image. Objects in image should be classified and its knowledge should be presented ontologically. This paper describes a way of presenting knowledge in fruit images ontologically. [1.2] INTRODUCTION TO ONTOLOGY: Ontology refers to organizing concepts and relations in domain. In other words, it is a way to represent domain knowledge in class-subclass manner and characterizing the relationship among classes and their instances. Ontology can be represented:- a) Graphically:--To represent ontologies in graph, every concept of domain is represented as node. Concepts can be noun. Further, nodes are connected by different kinds of links. The most 1

2 Ontology Based Knowledge Extraction From Fruit Images important kind of link is IS-A link which represent subclass-class relationship. Has-a provides information on the property of a class and possible values. strawberry Isa (rdfs:subclassof) Fruit (subclass-class relation) hascolour Deep red (property of Class) Figure 1: Graphical representation of Ontology b) Textually:-Most of the elements of an OWL ontology concern classes, properties, instances of classes, and relationships between these instances.the most basic concepts in a domain should correspond to classes that are the roots of various taxonomic trees.every individual in the OWL world is a member of the class owl:thing.for our sample fruits domain, we create one root class: Fruit. i. Representation of Classes: <owl:class rdf:id="fruit"/> or <owl:class rdf:id="fruits"></owl:class> The syntax rdf:id="fruit" is used to introduce a name, as part of its definition. This is the rdf:id attribute ([RDF]) that is like the familiar ID attribute defined by XML. Within this document, thefruit class can now be referred to using #Fruit, e.g. rdf:resource="#fruit ii. Representation Of Subclasses The fundamental taxonomic constructor for classes is rdfs:subclassof. It relates a more specific class to a more general class. If X is a subclass of Y, then every instance of X is also an instance of Y. The rdfs:subclassof relation is transitive <owl:classrdf:id="strawberry"> <rdfs:subclassof rdf:resource="#fruits" />... </owl:class> iii. Representation of Individuals(Objects) There are important issues regarding the distinction between a class and an individual in OWL. A class is simply a name and collection of properties that describe a set of individuals. Individuals are the members of those sets. <Strawberry rdf:id="alpine Strawberry" /> Alpine Strawberry is an individual because it denotes a single Strawberry varietal. iv. Representation Of Properties Properties let us assert general facts about the members of classes and specific facts about individuals.various types of properties can be defined like ObjectProperty, DatatypeProperty, rdfs:subpropertyof, rdfs:domain, rdfs:range,property restriction(allvaluesfrom, somevaluesfrom),cardinaility etc.) 2

3 International Journal of Computer Engineering and Applications, Volume X, Issue IV, April 16 ISSN A property is a binary relation. Two types of properties most frequently used are: datatype properties, relations between instances of classes and RDF literals and XML Schema datatypes i.e, it relates individuals to literal values object properties, relations between instances of two classes. i.e it relates individuals to other individuals. Domain and range are not constraints to be checked. They are axioms which are used by the reasoner to make inferences To create dataproperty:- <owl:class rdf:id="scientificname" /> <owl:datatypeproperty rdf:id="hasscientificname"> <rdfs:domain rdf:resource="#scientificname " /> <rdfs:range rdf:resource="&xsd;string"/> </owl:datatypeproperty> The hasscientificname property relates scientificname to string values To introduce the cultivatedin property, which relates strawberries to the regions they are located in. <owl:objectpropertyrdf:id="cultivatedin"> <rdfs:domain rdf:resource="#strawberry" /> <rdfs:range rdf:resource="#country" /> </owl:objectproperty> Notice how the domain and range of CultivatedIn are defined. The domain permits strawberry to be cultivated in a country Ontologies support reasoning that may be inheritance reasoning, transitivity reasoning and classification [1.3] RELATED WORK Many algorithms are developed which classify the fruit image automatically. Some existing systems which are based on fruit recognition and are referred for the proposed topic that analyze fruits using shape-based and colour-based analysis method have been reviewed. The existing systems are: 1. New Method for Fruits Recognition System. In New method for fruit recognition[2] has analyze, classify and identify the fruits images, which are selected and sent in to the system based on colour, shape and size features of the fruit. The KNN algorithm is the appropriate and effective classification algorithm to be used in the Fruits Recognition System. 2. Fruit Detection with Multiple Features using Fuzzy Logic[3] This method classifies and recognizes fruit images based on obtained feature values by using FCM(Fuzzy C-means). The fruit quality determination system analysis classifies and identifies fruits successfully 3

4 Ontology Based Knowledge Extraction From Fruit Images 3. Fruit Recognition using Color and Texture Features [4] The paper provides fusion of color and texture features for fruit recognition. The recognition is done by the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed sub- bands. 4. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine [5] The paper provides a novel classification method based on multi-class ksvm. The combination of color histogram, Unser s texture, and shape features are combined for classification of fruits. The proposed method presented in this paper is to enrich the classified fruit image with taxonomy of ontology, so that more information about the classified fruit is extracted. [1.4] BUILDING ONTOLOGY FOR FRUIT IMAGES Fruit Sweet, gently tangy Vitamin C Potassium Europe hastaste Is-a folate hasnativeplace hasnutrient fibre Strawberr y antioxidant Has shape conical Has Color Is cultivated in country haslargestproduction Has Scientific Name Fragaria X ananassa. hassecondlargestproduction Rich red USA SPAIN Figure 2: Ontology of Strawberry Fruit 4

5 International Journal of Computer Engineering and Applications, Volume X, Issue IV, April 16 ISSN As shown in Figure2, detailed knowledge is attached in form of ontology which describes features of fruit like its taste,colour, nutrients,largest production country, its scientific name etc. [1.5] SYSTEM ARCHITECTURE An Ontology based Approach system architecture describes the working of the various modules of the system and their interaction with each other while extracting knowledge. Two terms are used: Total Ontology Knowledge Base: It is used to describe the complete knowledge about a domain.e.g Total knowledge base about fruit would consist various types of fruits, along with their properties,their nutrients,their colour etc.total ontology knowledge base includes both concepts as well as relationships among them, and instances of the concepts too. Partial Ontology Knowledge Base: It is that portion of total ontology knowledge base, which includes knowledge about particular area only.it is considered as a sub-part of Total ontology. Various modules of system that help to extract knowledge are: 1. Input Image 2. Classification of image 3. Identifying Partial Ontology Classification(Based on Colour,Shape, Texture) Total Ontology Knowledge base Fig 3. Mapping from keyword to partial ontology 5

6 Ontology Based Knowledge Extraction From Fruit Images Input Image: In this step, image is provided to the system. The input image should contain single object about which user wants the detailed knowledge. Classification of image: In this step, image is classified to object by using one of the method described in section 1.3. Output of this step would be a keyword describing the object in image. As described earlier, the image is classified according to shape, colour, or texture or combination of all. Identifying Partial ontology: Once Image is classified using step2, image is then linked to its ontology, to extract detailed information.e.g if an image is classified as strawberry, then it is linked to partial ontology knowledge base representing details of strawberry fruit like its location, colour, taste, nutrients in it etc as shown in fig 3 above. [1.6]CONCLUSION This paper describes a way a) to build the ontology of knowledge behind an image b) to classify the image available on internet and linking image with its partial ontology to extract detail knowledge about it. Although many type of images are present on internet, but this paper specifically present ontology for fruit images.however, other types of images can also be analysed for knowledge extraction by building their ontologies. References [1] w3c recommendations on [2] New Method for Fruits Recognition System by Woo Chaw Seng, Seyed hadi mirisaee, International Conference on Electrical Engineering and Informatics 5-7 August 2009, Selangor, Malaysia. [3] Fruit Detection with Multiple Features using Fuzzy Logic by Monika Sharma, Vibhuti P N Jaiswal, Amit Goyal Electronics &Communication, MMU India, Volume 3, Issue 11, November 2013 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering [4] Fruit Recognition using Color and Texture Features by S.Arivazhagan, R.Newlin Shebiah, S.Selva Nidhyanandhan, L.Ganesan, VOL. 1, NO. 2, Oct 2010 E ISSN Journal of Emerging Trends in Computing and Information Sciences [5]. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine by Yudong Zhang and Lenan Wu, School of Information Science and Engineering, Southeast University, Nanjing , China; Sensors 2012, 12, ; [6] Semantic Image Retrieval: An Ontology Based Approach by Umar Manzoor, Mohammed A. Balubaid,Bassam Zafar, Hafsa Umar, M. Shoaib Khan, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No.4, 2015 [7] Ontology based Semantic Image Interpretation by Ivan donadello and Luciano serafini, Italy [8] Handbook on ontology by springer. 6