INTRODUCTION In recent years, the advancement in database management systems has enabled us to store and manage multimedia information. Multimedia information is important since the digital media is generated more than any other time now and there are many demands for extraction of the right information among this very big pile of data on the internet…
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There are different varieties of techniques being investigated in the area of information retrieval and the aim of this paper is to focus on the most popular and effective ones in extraction of images from multimedia databases. In general, we will discuss the two main research areas, using textual features and content based features but more specifically, we will discuss the technologies and advancements in employing the textual features. IMAGE RETRIEVAL There is a question of how to handle the vast growing multimedia information and the answer for that is to use multimedia databases. The aim is to retrieve images that most likely match with the query of a user. Users in general search for the images in databases using keywords and features like size, shape, location, etc. For retrieving the right information from such database using queries, two main approaches exist.  Firstly we could describe an image using a set of keywords but considering that manual labelling of all the multimedia information is a hard task. Another area of research which is at the centre of researcher’s attention is Content Based Image Retrieval (CBIR).  However, among these two main approaches, there is a question of efficiency of them. ...
TEXT BASE IMAGE RETRIEVAL In order to label images with the keywords, we could either do it manually or automatically, using computer algorithms trained based on the textual information of image and the image features.  However even in the automatic way, for training the computer we need to manually provide a set of training data for example different category of images. In addition to the constraints above, when we describe images using a set of keywords, a set of conflicts might happen do to some of the well known issues in the natural language, including synonym words, using different words to express one thing or using a word with usages in different contexts.   is one of the papers that suggests not using the text based feature alone since it is not going to be efficient due to the above natural language constraints. They recommend combining text features and visual features. There also some works that recently tried resolving the natural language constraints above.  The approach they used was Latent Semantic Analysis (LSA) used to joint feature vectors of textual and visual information of the images. The reason that the researchers mainly focus on the content based side of the research might be the problems that appears with labelling the data and of course the lack of accuracy caused by using general natural language processing techniques. Natural language processing is a complex task and full ability to process humans language has not been achieved yet. CONTEXT BASED IMAGE RETRIEVAL (CBIR) CBIR is a popular area of study in image retrieval these days. In this approach we index the images from their visual content. In other words we use features like shapes, texture
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“Image Retrieval Research Paper Example | Topics and Well Written Essays - 5000 Words”, n.d. https://studentshare.org/family-consumer-science/1405403-image-retrieval.
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