Ph.D, M.E, B-Tech

Dr. E. R. Vimina currently serves as Assistant Professor in the Department of Computer Science and I.T., School of Arts & Sciences, Amrita Vishwa Vidyapeetham, Kochi.

Qualification: Ph. D. (Computer Science), M. E. (Computer Science and Engineering), B. Tech. (Electrical and Electronics)


Publication Type: Journal Article

Year of Publication Title


Divya M. O. and E. R. Vimina, “Performance Analysis of Distance Metric for Content Based Image Retrieval”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6, 2019.[Abstract]

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

More »»


E. R. Vimina and K. Poulose Jacob, “Feature Fusion Method using BoVW Framework for Enhancing Image Retrieval”, IET Image Processing, vol. 13, no. 11, pp. 1979 – 1985, 2019.[Abstract]

The bag-of-visual words (BoVW) has been applied to myriad of recognition problems in computer vision such as object recognition, scene classification and image retrieval due to its scalability and high precision. However, their performance is subservient in certain datasets, especially in natural image datasets, mainly due to the lack of consideration of image cues such as colour, texture etc. which are not prime features while computing invariant descriptors, on which BoVW models are generally built on. Hence, this study describes a multi-cue fusion approach for BoVW framework, exploiting both early and late fusion methods, to improve the retrieval performance, mainly in natural image datasets. For this, a composite edge and colour descriptor is proposed to describe the local regions of the image along with the invariant feature descriptor Speeded Up Robust Features (SURF). Independent vocabularies are built based on these descriptors and images in the dataset are encoded to form two histograms using the respective vocabularies. The histograms are further fused to characterize the image. The retrieval is carried out by matching the histograms. Experimental results show that significant increment in the average precision can be attained by combining the proposed descriptor with invariant descriptors.

More »»