Evaluation of Images Using Various Distance Metrics
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 1
Abstract
Due to the digitization of data and advances in technology, it has become extremely easy to Obtain and store large quantities of data, particularly Multimedia data. Image data plays vital role in every aspect of the systems like business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. Fields ranging from Commercial to Military need to analyze these data in an efficient and fast manner. The need for image mining is high in view of such fast growing amounts of image data. Similarity and dissimilarity measures referred to as measures of proximity. Computing similarity measures are required in many data mining tasks. Categorical data, unlike numeric data, conceptually is deficient of default ordering relations on the attribute values. Devise distance metrics in data mining tasks for categorical data more challenging. Efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images by content . Features are extracted from images in pixel and compressed domains. The feature extraction and similarity measures are the two key parameters for retrieval performance. A similarity measure plays an important role in image retrieval. This paper compares different distance metrics such as Euclidean, Manhattan,Chebyshev, Canberra distances to find the best similarity measure for clustering the images.
Authors and Affiliations
Venkataramana Battula, Saritha Ambati
Keywords
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- DOI 10.9790/9622-0801052934.
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