LIBRARY MANAGEMENT SYSTEM WITH TOPIC MODELLING AND ITS ADAPTABILITY TO OPEN AND DISTANCE LEARNING LIBRARIES

The adoption of Integrated Library Systems (ILS) became prevalent in the 1980s and 1990s as libraries began or continued to automate their processes. These systems enabled library staff work, in many cases, more efficiently than in the past. However, these systems are restrictive and have thus undergone changes over the years, making processes more efficient. One area of improved capabilities is that of “search”, which in this paper, builds on integrating topic modeling as a new feature in modern integrated library systems in open and distance learning institutions. Users can now partake and explore new ways of resolving text classification and data exploration problems on a typical library management. This aims also at improving book search, browse and help in book-selection decision making

A Brief Review On Image Retrieval Techniques and its Scope

This paper presents the novel approach for image retrieval. Image retrieval is an important problem in many applications, such as copyright infringement detection, tag annotation, commercial retrieval, and landmark identification. Image retrieval definition is given and the concept and significance of image retrieval is also provided. Various image retrieval techniques based on content based, sketch based, also based on image annotation is explained here. The last section includes the approach for retrieval is given as a problem formulation

Discovering Multi-relational Latent Attributes by Visual Similarity Networks

Abstract. The key problems in visual object classification are: learning discriminative feature to distinguish between two or more visually similar categories ( e.g. dogs and cats), modeling the variation of visual appear-ance within instances of the same class (e.g. Dalmatian and Chihuahua in the same category of dogs), and tolerate imaging distortion (3D pose). These account to within and between class variance in machine learning terminology, but in recent works these additional pieces of information, latent dependency, have been shown to be beneficial for the learning process. Latent attribute space was recently proposed and verified to capture the latent dependent correlation between classes. Attributes can be annotated manually, but more attempting is to extract them in an unsupervised manner. Clustering is one of the popular unsupervised ap-proaches, and the recent literature introduces similarity measures that help to discover visual attributes by clustering. However, the latent at-tribute structure in real life is multi-relational, e.g. two different sport cars in different poses vs. a sport car and a family car in the same pose-what attribute can dominate similarity? Instead of clustering, a network (graph) containing multiple connections is a natural way to represent such multi-relational attributes between images. In the light of this, we introduce an unsupervised framework for network construction based on pairwise visual similarities and experimentally demonstrate that the constructed network can be used to automatically discover multiple dis-crete (e.g. sub-classes) and continuous (pose change) latent attributes. Illustrative examples with publicly benchmarking datasets can verify the effectiveness of capturing multi- relation between images in the unsuper-vised style by our proposed network.