Digital Image Processing

Neetesh Gupta ¹, R. K. Singh ², P. K. Dey ³

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 ITM University, Gurgaon, Haryana, IN
3 Jodhpur University (Jodhpur), IN

Abstract

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed.. This ever increasing amount of multimedia data creates a need for new stylish methods to get back the information one is looking for. Thus content-based image retrieval attracted many researchers of various fields. Retrieval of Images from Image library using appropriate features extracted from the content of Image is currently an active research area. For the intention of content-based image retrieval (CBIR) an up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is discussed. In this paper we propose A New Approach for Image Retrieval with interactive Relevance feedback based classification by Using Kernel Based Classifier .This Approach is applied to improve retrieval performance. Our aim is to select the most informative images with respect to the query image by ranking the retrieved images. This approach uses suitable feedback to repeatedly train the Histogram Intersection Kernel based Classifier. Proposed Approach retrieves mostly informative and correlated images.

Keywords

CBIR, Relevance Feedback, Color, Texture, Shape Feature Extraction.

Full Text

     

Neetesh Gupta ¹, Vijay Anant Athavale ², MD. Ilyas Khan ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 SVS Group of Institutions, Ganga Nahar, Hastinapur Road, Mawana, Merrut, IN

Abstract

Digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. While most traditional image retrieval systems perform searches using comparisons of text based strings, content based systems extract features from the content of an image to judge its similarity with another. For the purpose of image retrieval is presented in this paper. The image retrieval problem is motivated by the need to search the exponentially increasing space of image and video databases efficiently and effectively. We Extract Low level feature like as color, Texture, shape etc. and calculate similarity or dissimilarity between archieve of images. Finally we implement a user friendly Graphical system with Relevance feedback of image retrieval and finally quality assessment of similarity is evaluated.

Keywords

CBIR, Color, Texture, Shape Feature Extraction, Image Assessment, GUI, Similarity Measurement.

Full Text

     

Neetesh Gupta ¹, Niket Bhargava ², Md. Ilyas Khan ¹, Shiv Kumar ³, Bhupendra Verma ⁴

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 Department of Computer Science, Bansal Institute of Technology, Bhopal (M.P.), IN
3 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
4 Department of Computer Science, Technocrat Institute of Technology, Bhopal (M.P.), IN

Abstract

For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.

Keywords

Similarity, Measurement, CBIR, Low Level Feature Extraction, Correlation Coefficient.

Full Text