Narendra G. Bawane

Work place: S.B. Jain Institute of Technology, Management & Research, Nagpur, 441501, India



Research Interests: Speech Recognition, Image Processing, Network Security, Pattern Recognition, Neural Networks, Computer systems and computational processes


Narendra G. Bawane is currently working as a Principal, S.B. Jain Institute of Technology, Management and Research, Nagpur, India. He worked as a Head of the Computer Science and Engineering Department at G.H. Raisoni College of Engineering, Nagpur, India. He also worked with B.D. College of Engineering, Sewagram and Govt. Polytechnic, Nagpur, India. He has completed his B.E. from Nagpur University in 1987 and M. Tech. in 1992 from IIT, New Delhi. He completed his Ph.D. in 2006 at VNIT, Nagpur. He has total teaching experience of more than 25 years. His areas of interest include artificial neural network, wavelet analysis, image processing and emotion in speech and facial recognition, and hybrid intelligence.

Author Articles
An Interactive Approach for Retrieval of Semantically Significant Images

By Pranoti P. Mane Amruta B. Rathi Narendra G. Bawane

DOI:, Pub. Date: 8 Mar. 2016

Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.

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Image Retrieval by Utilizing Structural Connections within an Image

By Pranoti P. Mane Narendra G. Bawane

DOI:, Pub. Date: 8 Jan. 2016

Content-based image retrieval (CBIR) is broadly applicable for searching digital images from a gigantic database. Images are retrieved by their primitive visual contents such as color, texture, shape, and spatial layout. The approach presented in this paper utilizes structural connections within an image by integrating textured color descriptors and structure descriptors to retrieve semantically significant images. The retrieval results were obtained by applying the HSV histogram, color coherence vector, and local binary pattern histogram to the standard database of Wang et al., which has 1000 images of 10 different semantic categories. Euclidean distance was used to find the similarity between the query image and database images. This method was evaluated against different methods based on edge histogram descriptors, color structure descriptors, color moments, the color histogram, the HSV histogram, Tamura features, edge descriptors, geometrical shape attributes, and statistical properties such as mean, variance, skewness, and kurtosis. Retrieval results obtained using the proposed methods demonstrated a significant improvement in the average precision (73.8% and 73.1%) compared with those obtained using other existing retrieval methods.

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