V. Balamurugan

Bio: V. Balamurugan is an academic researcher from Madras Institute of Technology. The author has contributed to research in topics: Automatic image annotation & Content-based image retrieval. The author has an hindex of 1, co-authored 2 publications receiving 9 citations.

The new integrated color and texture based image retrieval using neuro-fuzzy approach

Abstract: In this paper we introduce the new integrated color and texture based image retrieval technique using neuro-fuzzy approach for content based image retrieval. Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we developed color and texture based neural network -fuzzy logic approach for content based image retrieval using 2D-wavelet transform. The system performance improved by the learning and searching capability of the neural network combined with the fuzzy interpretation. This overcomes the vagueness and inconsistency due to human subjectivity. Multiresolution analysis using 2D-DWT can decompose the image into components at different scales, so that the coarest scale components carry the global approximation information while the finer scale components contain the detailed information. The empirical results show that the precision improved from 67% to 98% and average recall rate of 67% to 98% for the general purpose database size of 10000 images compared with existing approaches.

Hybrid soft computing approaches to content based video retrieval

Abstract: Graphical abstractDisplay Omitted There has been an unrestrained growth of videos on the Internet due to proliferation of multimedia devices. These videos are mostly stored in unstructured repositories which pose enormous challenges for the task of both image and video retrieval. Users aim to retrieve videos of interest having content which is relevant to their need. Traditionally, low-level visual features have been used for content based video retrieval (CBVR). Consequently, a gap existed between these low-level features and the high level semantic content. The semantic differential was partially bridged by proliferation of research on interest point detectors and descriptors, which represented mid-level features of the content. The computational time and human interaction involved in the classical approaches for CBVR are quite cumbersome. In order to increase the accuracy, efficiency and effectiveness of the retrieval process, researchers resorted to soft computing paradigms. The entire retrieval task was automated to a great extent using individual soft computing components. Due to voluminous growth in the size of multimedia databases, augmented by an exponential rise in the number of users, integration of two or more soft computing techniques was desirable for enhanced efficiency and accuracy of the retrieval process. The hybrid approaches serve to enhance the overall performance and robustness of the system with reduced human interference. This article is targeted to focus on the relevant hybrid soft computing techniques which are in practice for content-based image and video retrieval.

A fuzzy approach for word level script identification of text in low resolution display board images using wavelet features

Abstract: Automated systems for understanding low resolution images of display boards are facilitating several new applications such as blind assistants, tour guide systems, location aware systems and many more. Script identification at character/word level is one of the very important pre-processing steps for development of such systems prior to further image analysis. In this paper, a new fuzzy based approach for word level script identification of text in low resolution images of display boards is presented. The proposed methodology uses horizontal run statistics and wavelet features for distinguishing 5 Indian scripts namely; Hindi, Kannada, English, Malyalam and Tamil. The method works in two phases; In the first phase, the wavelet transform based texture features such as zone wise wavelet energy features, vertical run statistical features of wavelet coefficients and wavelet log mean deviation features of decomposed energy bands at 2 levels are obtained from training word images and crisp sets are constructed, one for each script/language under study. The second phase is testing, in which test word image is processed to obtain horizontal run statistics to determine whether it belongs to Hindi script. Otherwise, the word image is processed to obtain a crisp vector. The degree of belongingness of crisp vector with each candidate object in the crisp sets is determined using newly devised fuzzy membership function. Further, fuzzy inference scheme is used to identify the script of the test word image. The proposed method is robust and insensitive to the variations in size and style of font, number of characters, thickness and spacing between characters, noise, and other degradations. The proposed method achieves an overall script identification accuracy of 94.33% and individual identification accuracy of 100% for Hindi Script, 98.67% for Kannada Script, 100% for English, 89% for Malyalam and 84% for Tamil Script.

Content based batik image classification using wavelet transform and fuzzy neural network

Abstract: In this paper we introduce the content-based image classification using wavelet transform with Daubechies type 2 level 2 to process the characteristic texture consisting of standard deviation, mean and energy as Input variables, using the method of Fuzzy Neural Network (FNN). All the input value will be processed using fuzzyfication with 5 categories namely Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be fuzzy input in the process of classification with neural network method. Batik images will be processed using 7 (seven) types of batik motif which is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process using FNN is Rule generation, such that for a new image of batik motif types can be immediately determined after FNN classification is completed. For the level of precision, this method is between 90-92%, including if we use the rule generation to determine the level precision is between 90-92%.

An Experiential Survey on Image Mining Tools, Techniques and Applications

Abstract: Digitization in every sector leads to the growth of digital data in a tremendous amount Digital data are not only available in the form of text but it is also available in the form of images, audio and video Decision making people in every field like business, public sector, hospital, etc are trying to get useful and implicit information from the already existing digital data bases Image mining is the concept used to extract implicit and useful data from images stored in the large data bases Image mining is used in variety of fields like medical diagnosis, space research, remote sensing, agriculture, industries and even in the educational field This paper elaborates the research works already done in image mining and also summarizes different tool developed, algorithms emerged and the applications of image mining used to extract the useful images in various fields

Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

Abstract: This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification. For the degree of precision of this method is 86-92%.