Human perception based image retrieval using emergence index and fuzzy similarity measure
- Authors: Deb, Sagarmay , Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, Melbourne, Victoria : 3rd-6th December 2007 p. 359-363
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- Description: The main concern dealing with content-based image retrieval (CBIR) is to bridge the semantic gap. The high level query posed by the user and low level features extracted by the machine illustrates the problem of semantic gap. To solve the problem of semantic gap, this paper presents a hybrid technique using an emergence index and fuzzy logic for efficient retrieval of images based on the colour feature. Emergence index (EI) is proposed to understand the hidden meaning of the image. Fuzzy similarity measure is developed to calculate the similarity between the target image and the images in the database. The images were ranked based on their similarity along with the fuzzy similarity distance measure. The preliminary experiments conducted on small set of images and promising results were obtained.
- Description: 2003004955
A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
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- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
Forecasting model for crude oil prices based on artificial neural networks
- Authors: Haidar, Imad , Kulkarni, Siddhivinayak , Pan, Heping
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 103-108
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- Description: This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design and appropriate selection of the training inputs, feedforward networks are capable of forecasting noisy time series with high accuracy.
- Description: 2003006659
Fingerprint feature extraction and classification by learning the characteristics of fingerprint patterns
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Network World Vol. 21, no. 3 (2012), p. 219-226
- Full Text: false
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- Description: This paper presents a two stage novel technique for fingerprint feature extraction and classification. Fingerprint images are considered as texture patterns and Multi Layer Perceptron (MLP) is proposed as a feature extractor. The same fingerprint patterns are applied as input and output of MLP. The characteristics output is taken from single hidden layer as the properties of the fingerprints. These features are applied as an input to the classifier to classify the-features into five broad classes. The preliminary experiments were conducted on small benchmark database and the found results were promising. The results were analyzed and compared with other similar existing techniques. © ICS AS CR 2011.
MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
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- Description: Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations. © 2012 IEEE.
- Description: 2003010699
Machine learning approach for content based image retrieval
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 1-11
- Full Text: false
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- Description: Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.
Natural language based fuzzy queries and fuzzy mapping of feature database for image retrieval
- Authors: Kulkarni, Siddhivinayak
- Date: 2010
- Type: Text , Journal article
- Relation: College of Computer Science & Informatics Vol. 4, no. 1 (2010), p. 11-19
- Full Text: false
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- Description: Fuzzy logic has been extensively used at various stages of image retrieval such as region groupings within the images as a feature extraction technique, for measuring the Natural Language based Fuzzy Queries and Fuzzy Mapping of Feature Database for Image Retrieval
Challenges of Challenges of Deploying RFID Technology for Reducing Medical Identity Theft
- Authors: Leicester, Phillip , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Infonomics Vol. 5, no. 3/4 (September/December 2012 2012), p. 597-602
- Full Text: false
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- Description: The healthcare industry is the biggest user of RFID technology due to its mobility in delivering data, tracking and surveillance of every individual, pathology tests, medications and the management of patient data. Because this technology is so vital within the healthcare/hospital environment this paper investigates and analyses the challenges as well as the issues facing RFID technology in implementing and providing security in guarding against the occurrences of medical identity theft. This form of identity theft is life threating as it adds medical data to a patients file who didn’t receive treatment for whatever conditions the imposter obtained as a result of their criminal activity. In preventing medical identity theft requires specific proposals from a policy, social and technological perspective.
- Description: C1
Image retrieval based on fuzzy mapping of image database and fuzzy similarity distance
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
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- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
Preface
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. xvi-xx
- Full Text: false
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- Description: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Hybrid technique for colour image classification and efficient retrieval based on fuzzy logic and neural networks
- Authors: Fernando, Ranisha , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Developments in the technology and the Internet have led to increase in number of digital images and videos. Thousands of images are added to WWW every day. To retrieve the specific images efficiently from database or from Internet is becoming a challenge now a day. As a result, the necessity of retrieving images has emerged to be important to various professional areas. This paper proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. Number of experiments was conducted for classification and retrieval of images on sets of images and promising results were obtained. The results were analysed and compared with other similar image retrieval system. © 2012 IEEE.
Machine learning algorithms for problem solving in computational applications: Intelligent techniques
- Authors: Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
- Full Text: false
- Reviewed:
- Description: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Emergence phenomenon and fuzzy logic in meaningful image segmentation and retrieval
- Authors: Deb, Sagarmay , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 167-178
- Full Text: false
- Reviewed:
- Description: Content-based image retrieval is a difficult area of research in multimedia systems. The research has proven extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one object, and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like colour, shape, texture, spatial relationships, and high-level definitions of the images is called the semantic gap. Until this problem is solved in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this chapter, the authors explore the possibilities of how emergence phenomena and fuzzy logic can help solve these problems of image segmentation and semantic gap.
Risk-based neuro-grid architecture for multimodal biometrics
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2010
- Type: Text , Conference proceedings
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- Description: Recent research indicates that multimodal biometrics is the way forward for a highly reliable adoption of biometric identification systems in various applications, such as banks, businesses, governments
Decision support based needs assessment for cancer patients
- Authors: Stranieri, Andrew , Kulkarni, Siddhivinayak , Macfadyen, Alyx , Love, Anthony , Vaughan, Stephen
- Date: 2011
- Type: Text , Conference paper
- Relation: Australasian workshop on health informatics and knowledge management (HIKM)
- Full Text: false
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- Description: Regular assessment of wellness or quality of life for patients throughout a cancer journey is important so as to identify aspects of life that could lead to distress and impede recovery or acceptance. The emerging trends in assessment are to deploy validated, quality of life instruments on touchscreen computers in medical waiting rooms. However, these add to workload of health care professionals and can be impersonal for patients to use. In this article, an alternate approach is presented that involves a decision support system with natural dialogue that elicits the patient's specific context in a far finer grained manner than is possible with questionnaire based instruments. The system includes a model of heuristics that health care professionals in a locality use to make inferences regarding a patient's quality of life and avenues for referral.
- Description: E1
Colour image annotation using hybrid intelligent techniques for image retrieval
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This paper presents a novel technique for colour image annotation based on neural networks and fuzzy logic. Neural network is proposed for classifying the images based on their contents and fuzzy logic is proposed for interpreting the content of an image in terms of natural language. One of the main aspects of this research is to avoid re-training of the neural networks by training the content of the image. Neural network is not trained on database of images; therefore image can be added or deleted from image database without affecting the training. The proposed hybrid technique is tested on real world colour image dataset and promising results are obtained. © 2012 IEEE.
- Description: 2003010700
Detection of child exploiting chatsfrom a mixed chat dataset as a text classification task
- Authors: Yearwood, John , Miah, Md Waliur Rahman , Kulkarni, Siddhivinayak
- Date: 2011
- Type: Text , Conference paper
- Relation: Proceedings of Australasian Language Technology Association Workshop
- Full Text: false
- Reviewed:
- Description: There is a rapidly growing body of work in the use of Embodied Conversational Agents (ECA) to convey complex contextual relationships through verbal and non-verbal communication, in domains ranging from military C2 to e-learning. In these applications the subject matter expert in often naive to the technical requirements of ECAs. ENGAGE (the Extensible Natural Gesture Animation Generation Engine) is desgined to automatically generate appropriate and 'realistic' animation for ECAs based on the content provided to them. It employs syntactic analysis of the surface text and uses predefined behaviours for the ECA. We discuss the design of this system, its current applications and plans for its future development.
The Impact of Biometric Systems on Communities: Perspectives and Challenges
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ACKMIDS 2008: Harnessing Knowledge Management to Build Communities, 11th Annual Australian Conference on Knowledge Management and Intelligent Decision Support p. 1-17
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Texture feature extraction and classification by combining statistical and neural based technique for efficient CBIR
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 Int. Conf. on MulGraB 2012, the 2012 Int. Conf. on BSBT 2012, and the 1st Int. Conf. on Intelligent Urban Computing, IUrC 2012, Held as Part of the Future Generation Information Technology Conference, FGIT 2012 Vol. 353 CCIS, p. 106-113
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- Description: This paper presents a technique based on statistical and neural feature extractor, classifier and retrieval for real world texture images. The paper is presented into two stages, texture image pre-processing includes downloading images, normalizing into specific rows and columns, forming non-overlapping windows and extracting statistical features. Co-occrance based statistical technique is used for extracting four prominent texture features from an image. Stage two includes, feeding of these parameters to Multi-Layer Perceptron (MLP) as input and output. Hidden layer output was treated as characteristics of the patterns and fed to classifier to classify into six different classes. Graphical user interface was designed to pose a query of texture pattern and retrieval results are shown. © 2012 Springer-Verlag.
- Description: 2003010656
Constructing an inter-post similarity measure to differentiate the psychological stages in offensive chats
- Authors: Miah, Md Waliur Rahman , Yearwood, John , Kulkarni, Siddhivinayak
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of the Association for Information Science and Technology Vol. 66, no. 5 (2015), p. 1065-1081
- Full Text: false
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- Description: Offensive Internet chats, particularly the child-exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach. Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter-post-similarity among the chat-posts within a particular behavioral stage and low inter-post-similarity across different behavioral stages. A psychological stage corpus-based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge-base to support the similarity measure. To find the inter-post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter-stage and intra-stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter-post similarity is used for clustering chat-posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods.