Image retrieval based on fuzzy mapping of image database and fuzzy similarity distance

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

Hybrid technique for colour image classification and efficient retrieval based on fuzzy logic and neural networks

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

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

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

Analysis of Green IT Features for Improving Business Performance

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Human perception based image retrieval using emergence index and fuzzy similarity measure

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

Forecasting model for crude oil prices based on artificial neural networks

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

Visual character N-grams for classification and retrieval of radiological images

Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases

Ethical Challenges and Lessons Learned During the Clinical Development of a Group A Meningococcal Conjugate Vaccine.

BACKGROUND: The group A meningococcal vaccine (PsA-TT) clinical development plan included clinical trials in India and in the West African region between 2005 and 2013. During this period, the Meningitis Vaccine Project (MVP) accumulated substantial experience in the ethical conduct of research to the highest standards. METHODS: Because of the public-private nature of the sponsorship of these trials and the extensive international collaboration with partners from a diverse setting of countries, the ethical review process was complex and required strategic, timely, and attentive communication to ensure the smooth review and approval for the clinical studies. Investigators and their site teams fostered strong community relationships prior to, during, and after the studies to ensure the involvement and the ownership of the research by the participating populations. As the clinical work proceeded, investigators and sponsors responded to specific questions of informed consent, pregnancy testing, healthcare, disease prevention, and posttrial access. RESULTS: Key factors that led to success included (1) constant dialogue between partners to explore and answer all ethical questions; (2) alertness and preparedness for emerging ethical questions during the research and in the context of evolving international ethics standards; and (3) care to assure that approaches were acceptable in the diverse community contexts. CONCLUSIONS: Many of the ethical issues encountered during the PsA-TT clinical development are familiar to groups conducting field trials in different cultural settings. The successful approaches used by the MVP clinical team offer useful examples of how these problems were resolved. CLINICAL TRIALS REGISTRATION: ISRCTN17662153 (PsA-TT-001); ISRTCN78147026 (PsA-TT-002); ISRCTN87739946 (PsA-TT-003); ISRCTN46335400 (PsA-TT-003a); ISRCTN82484612 (PsA-TT-004); CTRI/2009/091/000368 (PsA-TT-005); PACTR ATMR2010030001913177 (PsA-TT-006); PACTR201110000328305 (PsA-TT-007)