Image Retrieval based on Bag-of-Words model
It reviews existing methods for researchers and practitioners in computer vision, but is incremental as it does not introduce new techniques.
This survey provides an overview of the bag-of-words model for image retrieval, summarizing its application in large-scale systems that use local descriptors like SIFT for tasks such as object recognition and classification.
This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system. In recent years, large-scale image retrieval shows significant potential in both industry applications and research problems. As local descriptors like SIFT demonstrate great discriminative power in solving vision problems like object recognition, image classification and annotation, more and more state-of-the-art large scale image retrieval systems are trying to rely on them. A common way to achieve this is first quantizing local descriptors into visual words, and then applying scalable textual indexing and retrieval schemes. We call this model as bag-of-words or bag-of-features model. The goal of this survey is to give an overview of this model and introduce different strategies when building the system based on this model.