This chapter is a basic introduction to text information retrieval. Information Retrieval (IR) refers to the activities of obtaining information resources (usually in the form of textual documents) from a much larger collection, which are relevant to an information need of the user (usually expressed as a query). Practical instances of an IR system include digital libraries and Web search engines. This chapter presents the typical architecture of an IR system, an overview of the methods corresponding to the design and the implementation of each major component of an information retrieval system, a discussion of evaluation methods for an IR system, and finally a summary of recent developments and research trends in the field of information retrieval.
contourlet transform can extract image texture information more efficiently than wavelet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of contourlet transform retrieval system, a new feature named variance distribution was proposed and a contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.
With recent advances in wireless communication technologies, the world of mobile computing is flourishing with a variety of applications. This paper presents a mobile product information retrieval system that supports collaborative work among remote users. With the development of the system, a knowledge representation framework has been adopted which accommodates semantic relationships and similarity of product data. To illustrate the system developed, a case study in information retrieval for product design is presented．
Image retrieval plays an important role in a broad spectrum of applications. Contentbased retrieval (CBR) is one of the popular choices in many biomedical and industrial applications. Discrete image transforms have been widely studied and suggested for many image retrieval applications. The Discrete Wavelet Transform (DWT) is one of the most popular transforms recently applied to many image processing applications. The Daubechies wavelet can be used to form the basis for extracting features in retrieving images based on the description of a particular object within the scene. This wavelet is widely used for image compression. In this paper we highlight the common features between compression and retrieval. Several examples are used to test the DWT retrieval system. A comparison between DWT and Discrete Cosine Transform (DCT) is also made. The retrieval system using DWT requires preprocessing and normalization of images, which might slow down the retrieval process. The accuracy of the retrieval using DWT has been significantly improved by incorporating efficient K-Neighbor Nearest Distance (KNND) measure in our system.
Low rate of non-compliance to antituberculous therapy under the banner of directly observed treatment short course (DOTS) strategy and well organized retrieval system: a call for implementation of this strategy at all DOTS centers in Saudi Arabia
