Abstract
A picture or image is worth a thousand words. It is very much pertinent to the field of image processi ng. In the recent years, much advancement in VLSI technologies has triggered the abundant availability of powerful processors in the market. With the prices of RAM are having come down, the databases could be used to store information on the about art works, m edical images like CT scan, satellite images, natur e photography, album images, images of convicts i.e., criminals for security purpose, giving rise to a m assive data having a diverse image set collection. This le ads us to the problem of relevant image retrieval f rom a huge database having diverse image set collection. Web search engines are always expected to deliver flawless results in a short span of time including accuracy and speed. An image search engine also comes under the same roof. The results of an image search should match with the best available image from in the database. Content Based Image Retrieval (CBIR) has been proposed to enable these image search engines with impeccable results. In this CBIR technology, u sing only color and texture as parameters for zeroi ng in on an imagemay not help in fetching the best result . Also most of the existing systems uses keyword ba sed search which could yield inappropriate results. All the above mentioned drawbacks in CBIR have been addressed in this research. A complete analysis of CBIR including a combination of features has been carried out, implemented and tested.
Highlights
Content Based Image Retrieval (CBIR) has been evolving consistently for over a decade
Content Based Image Retrieval (CBIR) has been proposed to enable these image search engines with impeccable results. In this CBIR technology, using only color and texture as parameters for zeroing in on an imagemay not help in fetching the best result
The improvements in CBIR were based on Search engines are always expected to deliver flawless results in a short span of time
Summary
The improvements in CBIR were based on Search engines are always expected to deliver flawless results in a short span of time. Content Based Image Retrieval (CBIR) is augmentation to the existing existing image search strategy. Is a properties of the image like color, texture and shape. Few recent improvements in CBIR use relevance feedback methodology. In this method, the feedback from the user is used for filtering the images. The feedback from the user is used for filtering the images Limited parameters such as color and texture have been taken into consideration for constructing CBIR based search engines. Most of the existing systems uses keyword based search which could yield inappropriate results. A complete analysis of CBIR including a combination of features has glimpse of all the commercial tools studied
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