Image Retrieval Systems Essay examples

Image Retrieval Systems Essay examples

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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.
(i) These techniques are fully automatic and avoid the manual errors of text-based systems.
(ii) These techniques avoid complex tasks like annotation and also increase the accuracy of retrieval.
(iii) These techniques also reduce the amount of garbage, that is, irrelevant images retrieved.
(iv) These techniques, while computationally expensive, are more accurate than conventional image indexing.
In these systems, there exists a tradeoff between accuracy and computational cost. This tradeoff decreases as more efficient algorithms are utilized and increased computational power thus making it inexpensive. Moreover, the existing systems produce efficient results with small and medium sized image databases, but are generally ill-suited when applied to large sized image databases. This research work designs and proposes techniques to improve the process of image retrieval from large databases in terms of accuracy and speed. This chapter presents the proposed research methodology and introduces various techniques and methods used to develop the proposed CBIR systems.

3.1. RESEARCH DESIGN
In general, given an image space, F, Feature space, X, such that f : F  X (X = {x1, x2, …, xn}) and query image P, the proposed CBIR system retrieves a set of images M from image database S which satisfy Equation...


... middle of paper ...


...The techniques proposed in each of these phases are described in the subsequent sections.
3.2. PHASE I : PREPROCESSING
Preprocessing in CBIR systems is the process of preparing the template and query images into a form that improves the process of feature detection and image retrieval. The preprocessing step of the proposed CBIR system consists of two tasks, which are, color space conversion and image resizing. Popularly used color spaces include RGB (Red-Green-Blue) and HSV (Hue-Saturation-Value). Both the color space models produce good results but its performance degrades with complex scene images. As the color distance in the RGB space is not the same as human perception of color distance, the study transforms images from RGB color space to another modified space. A modified color spaces is proposed, which is based on RGB, HSV and YCbCr color space models.



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