No abstract

A comprehensive survey of scene classification based on pLSA formulation literature is presented. Due to the growth in robotics there is an increase in the concern towards visual technology adaption and the interest in the concern has been growing over past years. Vision creates the premises for brain-processing. Our brain receives and keeps unconscious processing over the stupendous amount of visual data every second. Scene Classification is the pioneer as well as a challenging discipline of the functional scope of Computer Vision. In this paper we have introduced the concept of different approaches for scene classification for enabling the viewing and classifying objects or scene around, the way human eyes do. The focus is based on the general overview of Scene Classification based on pLSA (probabilistic latent semantic analysis) integrated with different classifiers and algorithms. Each method is discussed to provide a complete reference for the description and comparison. Our aim is to highlight the rewarding direction for future research and real life applications of Scene Classification.

No abstract