Object Recognition using Bag of SIFT-based Features Model

Object Recognition has been one of the most increasingly important issues in Image Processing, finding applications in Computer Vision, Photography, Multimedia Retrieval, Detection and Data Classification. Based on the bag-of-words model, we demonstrate the efficiency of an advanced algorithm capable of performing objet recognition for both indoor and outdoor images. We use SIFT for retrieving objects’ local features, k-means Clustering for Codebook formation, train the system with SVM using some manually labeled images, and test the it with similar data set.