Compression-based self-organizing Recognizer Design

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Traditional object recognition schemes have been the statistical one wherein target objects’ statistical models are prepared manually and applied to unknown task data. Due to the heavy human intervention, this approach becomes weak for multi-media data with variety of target objects. In this research, a new autonomous object model acquisition scheme is investigated. For this, we investigate the possibility of data’s compressibility vector as a general feature, the possibility of co-occurrence-based object discovery and the possibility of highly autonomous recognition scheme based oh them. Promising experimental results are reported.