A Copy detection Method for Malayalam Text Documents using N-grams Model

In this paper a method of copy detection in short Malayalam text passages is proposed. Given two passages one as the source text and another as the copied text it is determined whether the second passage is plagiarized version of the source text. An algorithm for plagiarism detection using the n-gram model for word retrieval is developed and found tri-grams as the best model for comparing the Malayalam text. Based on the probability and the resemblance measures calculated from the n-gram comparison , the text is categorized on a threshold. Texts are compared by variable length n-gram(n={2,3,4}) comparisons. The experiments show that trigram model gives the average acceptable performance with affordable cost in terms of complexity

Analytical Study of Typographical Errors in OPACS and Corrective Measures

The present study is an attempt to highlight the problem of typographical errors in OPACS. The errors made while typing catalogue entries as well as importing bibliographical records from other libraries exist unnoticed by librarians resulting the non-retrieval of available records and affecting the quality of OPACs. This paper follows previous research on the topic mainly by Jeffrey Beall and Terry Ballard. The word “management” was chosen from the list of likely to be misspelled words identified by previous research. It was found that the word is wrongly entered in several forms in local, national and international OPACs justifying the observations of Ballard that typos occur in almost everywhere. Though there are lots of corrective measures proposed and are in use, the study asserts the fact that human effort is needed to get rid of the problem. The paper is also an invitation to the library professionals and system designers to construct a strategy to solve the issue

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Analytical Study of Typographical Errors in OPACS and Corrective Measures

The present study is an attempt to highlight the problem of typographical errors in OPACS. The errors made while typing catalogue entries as well as importing bibliographical records from other libraries exist unnoticed by librarians resulting the non-retrieval of available records and affecting the quality of OPACs. This paper follows previous research on the topic mainly by Jeffrey Beall and Terry Ballard. The word “management” was chosen from the list of likely to be misspelled words identified by previous research. It was found that the word is wrongly entered in several forms in local, national and international OPACs justifying the observations of Ballard that typos occur in almost everywhere. Though there are lots of corrective measures proposed and are in use, the study asserts the fact that human effort is needed to get rid of the problem. The paper is also an invitation to the library professionals and system designers to construct a strategy to solve the issue

Archiving News In A University Using Open Source Software

Newspapers cover a large amount of information everyday on topics of varied interests. To a university, newspapers are essential components of communication as they cover various happenings in a university. These items of information are neither stored properly nor put in retrieval systems for future use. The news and views appeared in newspapers can effectively be organized in a digital library making use of open source software. The CUSAT digital library (http://dspace.cusat.ac.in/dspace/) has organized some news items that appeared in local newspapers about the university under a special community named “CUSAT-News”. This article describes the methods of collecting, selecting, organizing, providing access and preserving news items required by a university using DSpace open source software.

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications