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We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize
in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
In the past few years, videos become an ordinary communication mean for both personal and business activities. Not only the keyword search that have been utilized widely, but also the video content-based search, i.e. given a query video, the similar video sequences can be retrieved. Meanwhile, the increasing of the
paper to descript how to builds the cross-linking-index each other among melody, pitch, keyword of lyrics, and picture. The proposed CDIndex can be extended applied to cross-media information retrieval system in digital multimedia database or over the Internet. It will improve the user's experience by applying this new
the actual content of the image. The term dasiacontentpsila in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, search must rely on metadata such as captions or keywords, which may be laborious or
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.