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collections was using keyword metadata, or simply by browsing. Nowadays, content based images retrieval (CBIR) is the way to assist the system to retrieve the related images. When the users are not satisfied with their query results, the relevance feedback (RF) retrieval is one of the solutions for this problem. The user needs
integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper
the document tags is considered as cluster name. Thus in short, web search results that are fetched from the prevailing web search engines grouped under phrases that contain one or more search keywords. This paper aims at organizing web search results into clusters facilitating quick browsing options to the browser
Finding information based on an object's profile is very useful when exact keywords for the object are unknown. Current image retrieval system all ignores the color information, for example we want to find a super-star with a piece of red petticoat, or we want to a red flower with white background. They all cannot
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.