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
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.
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
- Full Text:
- 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