1 CS 430 / INFO 430 Information Retrieval Lecture 17 Metadata 4 2 Course Administration 3 Automated Creation of Metadata Records Sometimes it is possible to generate metadata automatically from the content of a digital object. The effectiveness varies from field to field. Examples Images -- characteristics of color, texture, shape, etc. (crude) Music -- optical recognition of score (good) Bird song -- spectral analysis of sounds (good) Fingerprints (good) 4 Automated Information Retrieval Using Feature Extraction Example: features extracted from images Spectral features: color or tone, gradient, spectral parameter etc. Geometric features: edge, shape, size, etc. Textural features: pattern, spatial frequency, homogeneity, etc. Features can be recorded in a feature vector space (as in a term vector space). A query can be expressed in terms of the same features. Machine learning methods, such as a support vector machine, can be used with training data to create a similarity metric between image and query Example: Searching satellite photographs for dams in California 5 Example: Blobworld 6 7 8 Automatic Creation of Surrogates for Non-textual Materials Discovery of non-textual materials usually requires surrogates How far can these surrogates be created automatically? Automatically created surrogates are much less expensive than manually created, but have high error rates. If surrogates have high rates of error, is it possible to have effective information discovery? 9 Example: Informedia Digital Video Library Collections: Segments of video programs, e.g., TV and radio news and documentary broadcasts. Cable Network News, British Open University, WQED television. Segmentation: Automatically broken into short segments of video, such as the individual items in a news broadcast. Size: More than 4,000 hours, 2 terabyte. Objective: Research into automatic methods for organizing and retrieving information from video. Funding: NSF, DARPA, NASA and others. Principal investigator: Howard Wactlar (Carnegie Mellon University). 10 Informedia Digital Video Library History Carnegie Mellon has broad research programs in speech recognition, image recognition, natural language processing Basic mock-up demonstrated the general concept of a system using speech recognition to build an index from a sound track matched against spoken queries. (DARPA funded.) Informedia developed the concept of multi-modal information discovery with a series of users interface experiments. (NSF/DARPA/NASA Digital Libraries Initiative.) Continued research particularly in human computer interaction. Commercial spin-off failed. 11 The Challenge A video sequence is awkward for information discovery: Textual methods of information retrieval cannot be applied Browsing requires the user to view the sequence. Fast skimming is difficult. Computing requirements are demanding (MPEG-1 requires 1.2 Mbits/sec). Surrogates are required 12 Multi-Modal Information Discovery The multi-modal approach to information retrieval Computer programs to analyze video materials for clues e.g., changes of scene methods from artificial intelligence, e.g., speech recognition, natural language processing, image recognition. analysis of video track, sound track, closed captioning if present, any other information. Each mode gives imperfect information. Therefore use many approaches and combine the evidence. 13 Multi-Modal Information Discovery With mixed content and mixed metadata, the amount of information about the various resources varies greatly but clues from many difference sources can be combined. "The fundamental premise of the research was that the integration of these technologies, all of which are imperfect and incomplete, would overcome the limitations of each, and improve the overall performance in the information retrieval task." [Wactlar, 2000] 14 Informedia Library Creation Video Audio Text Speech recognition Image extraction Natural language interpretation Segmentation Segments with derived metadata 15 Informedia: Information Discovery User Segments with derived metadata Browsing via multimedia surrogates Querying via natural language Requested segments and metadata 16 Text Extraction Source Sound track: Automatic speech recognition using Sphinx II and III recognition systems. (Unrestricted vocabulary, speaker independent, multi-lingual, background sounds). Error rates 25% up. Closed captions: Digitally encoded text. (Not on all video. Often inaccurate.) Text on screen: Can be extracted by image recognition and optical character recognition. (Matches speaker with name.) Query Spoken query: Automatic speech recognition using the same system as is used to index the sound track. Typed by user 17 Image Understanding Informedia has developed specialized tools for various aspects of image understanding scene break detection segmentation icon selection image similarity matching camera motion and object tracking video-OCR (recognize text on screen) face detection and association 18 Multimodal Metadata Extraction 19 Speech Recognition: An Evaluation Experiment Test corpus: 602 news stories from CNN, etc. Average length 672 words. Manually transcribed to obtained accurate text. Speech recognition of text using Sphinx II (50.7% error rate) Errors introduced artificially to give error rates from 0% to 80%. Relative precision and recall (using a vector ranking) were used as measures of retrieval performance. As word error rate increased from 0% to 50%: Relative precision fell from 80% to 65% Relative recall fell from 90% to 80% 20 Speech recognition and retrieval performance 21 User Interface Concepts Users need a variety of ways to search and browse, depending on the task being carried out and preferred style of working Visual icons one-line headlines film strip views video skims transcript following of audio track Collages Semantic zooming Results set Named faces Skimming 22 23 Thumbnails, Filmstrips and Video Skims Thumbnail: A single image that illustrates the content of a video Filmstrip: A sequence of thumbnails that illustrate the flow of a video segment Video skim: A short video that summarizes the contents of a longer sequence, by combining shorter sequences of video and sound that provide an overview of the full sequence 24 Creating a Filmstrip Separate video sequence into shots Use techniques from image recognition to identify dramatic changes in scene. Frames with similar color characteristics are assumed to be part of a single shot. Choose a sample frame Default is to select the middle frame from the shot. If camera motion, select frame where motion ends. User feedback: Frames are tied to time sequence. 25 Creating Video Skims Static: Precomputed based on video and audio phrases Fixed compression, e.g., one minute skim of 10 minute sequence Dynamic: After a query, skim is created to emphasize context of the hit Variable compression selected by user Adjustable during playback 26 Limits to Scalability Informedia has demonstrated effective information discovery with moderately large collections Problems with increased scale: Technical -- storage, bandwidth, etc. Diversity of content -- difficult to tune heuristics User interfaces -- complexity of browsing grows with scale 27 Lessons Learned Searching and browsing must be considered integrated parts of a single information discovery process. Data (content and metadata), computing systems (e.g., search engines), and user interfaces must be designed together. Multi-modal methods compensate for incomplete or error- prone data.