Syeda Fiza - Academia.edu (original) (raw)
I have completed my Intermediate from IMCG.And doing bachelors in Business Administration from Capital University of Science and Technology Islamabad.
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Papers by Syeda Fiza
International journal of engineering research and technology, 2018
In this paper, the problem of efficiently extracting low level features for image representation ... more In this paper, the problem of efficiently extracting low level features for image representation in content based image retrieval is addressed. Wavelet transform being very popular and effective in representing objects with isolated point singularities, but failed to represent line singularities. We introduce a novel framework to extract appropriate features like color, shape and texture representing edges and other singularities along lines which effectively fills the gap between high level and low level semantics. Ridgelet transform is applied on a segmented image and statistical features such as mean and standard deviation are extracted from each of the Ridgelet sub-bands there by generating feature vectors. PCA is applied to reduce the dimensionality of the data and Euclidian distance is used as similarity measure between database and query images. The algorithm also supports for the retrieval of similar images to the given query image from Google web server. Four benchmarking d...
International journal of engineering research and technology, 2018
In this paper, the problem of efficiently extracting low level features for image representation ... more In this paper, the problem of efficiently extracting low level features for image representation in content based image retrieval is addressed. Wavelet transform being very popular and effective in representing objects with isolated point singularities, but failed to represent line singularities. We introduce a novel framework to extract appropriate features like color, shape and texture representing edges and other singularities along lines which effectively fills the gap between high level and low level semantics. Ridgelet transform is applied on a segmented image and statistical features such as mean and standard deviation are extracted from each of the Ridgelet sub-bands there by generating feature vectors. PCA is applied to reduce the dimensionality of the data and Euclidian distance is used as similarity measure between database and query images. The algorithm also supports for the retrieval of similar images to the given query image from Google web server. Four benchmarking d...