Performance Evaluation of Raster Based Shape Vectors in Object Recognition
This work addresses object recognition in computer vision and multimedia retrieval, but it appears incremental as it focuses on performance evaluation of existing raster models.
The paper tackled the problem of object recognition by evaluating circular and spiral raster models for shape vectors, reporting results on average retrieval efficiency and computational cost.
Object recognition is still an impediment in the field of computer vision and multimedia retrieval.Defining an object model is a critical task. Shape information of an object play a critical role in the process of object recognition. Extraction of boundary information of an object from the multimedia data and classifying this information with associated objects is the primary step towards object recognition. Rasters play an important role while computing object boundary. The trade-off lies with the dimensionality of the object versus computational cost while achieving maximum efficiency. In this treatise an attempt is made to evaluate the performance of circular and spiral raster models in terms of average retrieval efficiency and computational cost.