Wednesday 1 May 2019

SCENE CLASSIFICATION USING Recurrent CNN Technique

SCENE CLASSIFICATION USING Recurrent CNN Technique

Summary

Global Positioning systems are now moving forward with Vision based positioning systems. Camera is being used in all places for safety and security reasons. The research on image based data extraction is always adaptive in which scene classification is one of the criteria.

Scene classification is nothing but extracting the maximum information for the input captured scene image( Casual image captured with Landmark or Text info etc) The maximum information we mean it as text info from the name board of the coffee shop, number plate of the car, name board of the bus stop etc any text.Image info also consists of Land mark of the place such as a temple, a famous building etc



Image processing technique is used basically to extract the information about the input image such as shape, color, text, unique features such as corners, lines, any geometrical shapes etc Extracting those info we call as pre-processing techniques.

Next step is segmentation of the feature extracted info such as any particular shape present then it is to be segmented from the background to analyze it Separately.The text segmentation is another work. extracting the text component alone from the image and converting it into sharpened text image and finally comparing the image components using OCR technique to get the text info completely.

Using the obtained info as feature vectors the input images are compared with the database image and finally using an Recurrent Convolutional Neural Network method we classify the image with database to find out the best matching score.A prototypical model is developed using MATLAB software and results are shown below.

PREPROCESSING


FEATURE EXTRACTION

RCNN MODEL



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