By Mark T. Maybury
The appearance of more and more huge shopper collections of audio (e.g., iTunes), imagery (e.g., Flickr), and video (e.g., YouTube) is riding a necessity not just for multimedia retrieval but in addition details extraction from and throughout media. additionally, commercial and executive collections gasoline necessities for inventory media entry, media maintenance, broadcast information retrieval, id administration, and video surveillance. whereas major advances were made in language processing for info extraction from unstructured multilingual textual content and extraction of items from imagery and video, those advances were explored in principally self reliant study groups who've addressed extracting details from unmarried media (e.g., textual content, imagery, audio). And but clients have to look for ideas throughout person media, writer multimedia artifacts, and practice multimedia research in lots of domains.
This assortment is meant to serve numerous reasons, together with reporting the present state-of-the-art, stimulating novel learn, and inspiring cross-fertilization of detailed study disciplines. the gathering and integration of a standard base of highbrow fabric will supply a useful carrier from which to coach a destiny new release of move disciplinary media scientists and engineers.
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Extra resources for Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring
Sample text
Accordingly, today, many commercial or open source information extraction solutions are available, such as Bolt Baranek and Neuman’s IdentiFinder™ (Cambridge, MA), IBM’s Unstructured Information Management Architecture (UIMA) (New York), Rocket Software’s Aerotext™ (Newton, MA), Inxight’s ThingFinder (Sunnyvale, CA), MetaCarta GeoTagger (Cambridge, MA), SRA’s NetOwl Extractor (Fairfax, VA), and others. 3 AUDIO EXTRACTION Just as information extraction from text remains important, so too there are vast audio sources from radio to broadcast news to audio lectures to meetings that require audio information extraction.
741 on the more difficult one. For the detection task, 50% overlap in bounding boxes was considered a success with multiple detections considered as (one true +) false positive with average precision (AP) as defined by TREC (mean precision interpolated at various recall levels). 195 on the hard test, with significant variance across the classes. 3 for motorbikes. 4. In summary, there was more encouraging performance on cars and motorbikes than people and bicycles. , sliding window, combination with whole-image classifiers, segmentation-based).
The best of six 2D person (including arms and legs) tracking systems in video surveillance achieved 55% accuracy and 63% precision. The best moving vehicle tracker achieved about 70% accuracy and 61% precision. Visual and acoustic far afield person identification in meetings (28 people) were measured on 1-, 5-, 10-, and 20-second test segments on 200 ms annotated visual data. The best visual results achieved 84–96% accuracy on the easiest and hardest tests, an over 10% increase over the previous year.