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Feature Selection and Extraction

In many machine learning and pattern recognition problems, the number of attributes are too large that the learning process becomes complicated with degraded performance. To alleviate this problem, depending on the problems to be solved, one may select good attributes from the available input attributes or extract new features by combining the existing attributes. The former is known as input feature selection while the latter is called as feature extraction.

We have tackled the feature selection and extraction problems extensively and proposed the following algorithms.

Object Recognition

Object Detection

Rainbow SSD "Enhancement of SSD by concatenating feature maps for object detection", BMVC2017, London, UK, Sep. 2017. (Code available)

Object Tracking