Annotated Bibliography On Digital Libraries

These 16 features included 12 features calculated based on the 6 multispectral bands, which is mean value and standard deviation of these bands. In addition, we chose intensity, texture-variance, texture-mean, and NDVI (Normalized Difference Vegetation Index) for classifications. Finally, training samples were selected for each classification category based on the previously segmented and merged objects

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

The author of this article Mrs. Denise Davis is the director of the Office for Research and Statistics of the American Library Association. This means that Mrs. Davis is able to access and is responsible for the reports mandatory for each library across America to turn in each fiscal year, these reports are used to determine what changes the libraries need to make to better serve the public. Given her credibility to write on such topics she then chooses a topic each year to write on to prepare libraries and librarians across the country for the upcoming years. In 2010 Mrs. Davis choose to write on eBooks and their increasing popularity throughout the American population as well as the difficulties libraries have encountered in the past fiscal year to compete with eBooks. Because this article is written in 2010 it shows just how long libraries have struggled with the topic of eBooks and can demonstrate the new policies that have been used by the libraries throughout the past four years. This information can help my research in showing how eBooks have affected the library's behavior, and consequently give me the statistics needed to show the effects of eBooks on the libraries throughout the recent years. Understanding this source is outdated other sources will also likely be used in my final project from the American Library Association websites of statistics of more recent years.

The basic principle of this algorithm is to recognize the input paper currency. First of all acquired the image from a particular source. As in this thesis we use for reference images. System read the particular image. Then resize the image. After that the color separator convert the image into RGB to Gray scale and then in binary image. After that the system use color noise median filter. The currency length detector detects the length of the currency. Using the feature extraction techniques the system detect the particular feature of that currency and then the system use pattern matching algorithm to math that particular feature. The input image match with particular database image and according to that we find the currency. In this way this thesis design a automatic system in which we can recognized the paper currency.

Abstract - Content based image retrieval is an image search technique which uses content of features such as colour, texture, shape, etc. to find relevant images from large collection of data according to user’s request query image on the basis of similarity in features or content that means feature from query image compared with features from image database. The problem arises that large number of features may cause high dimensionality curse. To avoid dimensionality curse, feature selection method is used

It is based on Local Binary Patterns (LBP) features [3], which have shown to be a powerful and computationally efficient feature extractor. Then Chi-square based classification method is used for Authentication.

There are myriad ways a child may develop a love of reading, however in the modern era it seems that this passion and hunger for not only knowledge but for the pure enjoyment derived from it has been all but lost. I have children of my own that are not old enough to begin to approach about this and most other children I see are too endeavored with their tablets, videogames and social media to be concerned with it. I can therefore only speak from the one common frame of reference I have and that is myself. In doing so, I will inform you of just how a child may develop a love of reading and possibly shed some light on how children may come to have a love of reading even now.

In today’s revolution oriented environment, multimedia contents play a vital role in a wide range of applications, products and services. The high usage of these contents demand efficient searching and indexing for users. This demand has drawn substantial research attention towards image retrieval systems in the last few decades. Many great methods have been proposed, which offer numerous advantages like the following.

can contain elements with only a partial degree of member-ship.The main power of fuzzy logic image processing is in

Abstract— This paper proposed two set of edge-texture features is such as Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for object recognition. The proposed DLBP and DRLTP are resultant from the disadvantage of the Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP).The LBP system and the RLBP system are mapped in the same block .The proposed feature indentifies the problem of discrimination between a bright object against dark background and vice-versa. The proposed feature also retains contrast information for object contours the LBP, LTP and RLBP discards. By the proposed features the objects is further analyzed for the accurate position of the object in the given image.

A binary image is the matrix comprising of only 0’s(black) and 1’s(white). It can be implemented by using the command im2bw in MATLAB. Thresholding concept has been used in this algorithm. The threshold concept works by choosing a threshold value, T, automatically and then extract (or separate) object from background.

Shape is a characteristic of an object (like colour or texture, for example) which allows many numerical characterizations. Such numerical characteristics enable a transformation of a difficult object comparison problem onto a, much easier, vector comparison problem. Precisely, feature vectors, whose coordinates are numerically evaluated shape characteristics, corresponded to considered objects. Then, the similarity among the objects is evaluated based on the difference between their corresponding feature vectors. Obviously, such comparison is very suitable in computer supported data manipulation tasks. Shape properties like convexity, rectangularity, compactness, etc., can be evaluated numerically in several ways [65].

In supervised classification, identifying feature cover types, selecting high-quality and sufficiently numerous training sample sets is the time consuming and one of the major challenges that the investigators face during digital image