Visual Information Retrieval: the Next challenge in Information Management - ERM Expert... - 0 views
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In the past 20 years, a lot of research has been done towards visual information retrieval on pictures and video files. Not all of it has been successful. But on the last years, the quality of these visual search engines has reached levels that are beginning to be acceptable for eDiscovery, compliance, law enforcement and intelligence applications.
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More and more electronically stored information (ESI) is non-text based or does not contain any searchable text components: sound recordings, video and pictures are growing exponentially in size and more and more collaborative and social network applications support (only) these information formats.
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In addition, a whole generation is growing up that no longer uses written communication forms such as letters or emails: they only use social networks and other new media forms for communication and collaboration.
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Electronic files containing one of more text components or embedded objects with text components can be searched by using text-based queries.
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Document scans (images) and even pictures can be enriched with the text of the original document or even with recognizable logo’s in the pictures. The same technology can also be applied to video shots.
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Audio and the audio component of a video file can be processed by a phonetic search engine and users can search the content by looking for specific words or phoneme sequences.
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In addition, audio-, pictures- and video files can be searched on contextual information such as the file name, added meta-information or text that surrounds the picture or the video on a web page.
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Web search engines such as Google, Bing and Yahoo use primarily contextual text information from pictures and video’s to search on these object. This text can be tagged by users or can be found in the file name, file location, surrounding text on the webpage, etc. In some cases, words that are recognized in the images and videos with Optical Character Recognition (OCR) technology is used, or nudity is recognized and filtered, but that is about it. There is not or limited influence from pure visual information retrieval technology such as: give me all outdoor pictures or all images with a helicopter in it.
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State-of-the-art visual search technology should address all of these aspects and support both text-based as image or video example based querying, result navigation and viewing.
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Ranking images is based on complex statistics and other mathematical properties that are not always intuitive to humans. Users need a much more exploratory and visual result list that uses all available dimensions when searching images and videos.
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There are many use cases in the field of visual information retrieval varying from searching pictures on the internet to recognizing faces of hooligans at the entrance of a high risk football match, monitoring airports with surveillance cameras and investigating child abuse.
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Many of these applications are highly specialized applications requiring a lot of specialized knowledge and experience to work effectively.
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However, I expect that in the next year or five, real visual information retrieval will become a core component of in-house Enterprise Information Management systems as more and more information consists of pictures and videos that are not annotated and therefore hard to find.