Fusing Local Image Descriptors for Large-Scale Image Retrieval

Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned in an unsupervised fashion, and each image is modeled as a mixture of topics/object parts depicted in the image. However, not all object classes will benefit from all visual descriptors. Therefore, we also investigate which visual descriptor (set) is most appropriate for each of the twelve classes under consideration. We evaluate the presented models on a real world image database consisting of more than 246,000 images.