Computer Vision Based Object Detection and Recognition System for Image Searching

Computer Vision is a concept which works with the methods for automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. To fulfill the new challenges, the system which is proposed in this paper, is mainly being used for object detection and recognition of images so that the image search engine becomes more fruitful. A large dataset of 15 million high-resolution images have been taken from ImageNet with more than 21K different classes to the purpose of object detection and recognition system for image search engine which have been trained through machine learning. TensorFlow is an open source library for software which uses machine learning techniques as Convolution Neural Network to train datasets and to provide the percentage accuracy of objects in an image. The datasets have been trained so that the objects can be detected and figured out. The trained datasets create a protocol buffer (.pb) file which ensures the serialization of datasets according to their classes for matching the patterns. For giving input as well as viewing the result with best guesses, it has been designed a demo image search engine using Laravel5.4 framework of PHP language. By uploading several images on the demo image search engine, best guesses can be shown where the input image matches the pattern with protocol buffer file to provide the output results. There has been achieved highest accuracy rate of 99.983% best guesses and lowest accuracy rate of 89.282% best guesses for this proposed system which is better than the previous state-of-art.