Outlook For Content-Based Retrieval - MULTIMEDIA

A recent overview collecting the very latest ideas in content - based retrieval identified the following present and future trends: indexing, search, query, and retrieval of multimedia data based on

  1. Video retrieval using video features: image color and object shape, video segmentation, video keyframes, scene analysis, structure of objects, motion vectors, optical flow (from computer vision), multispectral data, and so - called "signatures" that summarize the data
  2. Spatiotemporal queries, such as trajectories
  3. Semantic features; syntactic descriptors
  4. Relevance feedback, a well - known technique from information retrieval
  5. Sound, especially spoken documents, such as using speaker information
  6. Multimedia database techniques, such as using relational databases of images
  7. Fusion of textual, visual, and speech cues
  8. Automatic and instant video manipulation; user - enabled editing of multimedia databases
  9. Multimedia security, hiding, and authentication techniques such as watermarking.

This field is truly rich and meshes well with the outline direction of MPEG - 7.

In another direction, researchers try to create a search profile to encompass most instances available, say all "animals". Then, for relational database searches, such search profiles are communicated via database queries. For searches using visual features, intelligent search engines learn a user's query concepts through active learning. This type of endeavor is called "query-based learning".

Another approach focuses on comprehending how people view images as similar, on the basis of perception. The function used in this approach is a type of "perceptual similarity measure" and is learned by finding the best set of features (color, texture, etc.) to capture "similarity" as defined via the groups of similar images identified.

Garden video: (a) frames from a digital video; (b) keyframes selected

Garden video: (a) frames from a digital video; (b) keyframes selected

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