A Review on Advanced Mechanism in Learning and Recognition of Ops from Weakly Labeled Street View Images

Abstract

Mobile phones are powerful image and video processing device containing the other various features like high-resolution cameras, display color, and hardware-accelerated graphics. The various applications give rise to a key technique of daily life visual object recognition. On premise sign is a popular form of commercial advertising, widely used in our daily life. The OPSs containing visual diversity associate with complex environmental conditions. Observing that, todays in most of existing image data set OPSs characteristics are lacking. In this, first proposed an OPS-62 data set, in which totally 4649 OPS images of 62 different businesses are collected from Googles Street View. For addressing the problem of real-world OPS learning and recognition, was developed a probabilistic framework based on the distributional clustering, in order to exploit the distributional information of each visual feature. Learning OPS images for more accurate recognitions and less false alarms. Main approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large scale multimedia applications.