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
- Full Text:
- 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
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
- Full Text:
- 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