The Role of Sparse Data Representation in Semantic Image Understanding

The Role of Sparse Data Representation in Semantic Image Understanding Author: Artur Przelaskowski Keywords: Computational intelligence, image understanding, sparse image representation, nonlinear approximation, semantic information theory

Abstract:

This paper discusses a concept of computational understanding of medical images in a context of computer-aided diagnosis. Fundamental research purpose was improved diagnosis of the cases, formulated by human experts. Designed methods of soft computing with extremely important role of: a) semantically sparse data representation, b) determined specific information, formally and experimentally, and c) computational intelligence approach were adjusted to the challenges of image-based diagnosis. Formalized description of image representation procedures was completed with exemplary results of chosen applications, used to explain formulated concepts, to make them more pragmatic and assure diagnostic usefulness. Target pathology was ontologically described, characterized by as stable as possible patterns, numerically described using semantic descriptors in sparse representation. Adjusting of possible source pathology to computational map of target pathology was fundamental issue of considered procedures. Computational understanding means: a) putting together extracted and numerically described content, b) recognition of diagnostic meaning of content objects and their common significance, and c) verification by comparative analysis with all accessible information and knowledge sources (patient record, medical lexicons, the newest communications, reference databases, etc.).

Interesting in its own right for image analysis in the important area of medical imaging but caught my eye for another reason.

Sparse data representation works for understanding images.

Would it work in other semantic domains?

Questions:

  1. What are the minimal clues that enable us to understand a particular text?
  2. Can we learn those clues before we encounter a particular text?
  3. Can we create clues for others to use when encountering a particular text?
  4. How would we identify the text for application of our clues?

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