Image retrieval on large-scale image databases

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. In this work we will employ the image content as a source of information to retrieve images. We study the representation of images by Latent Dirichlet Allocation (LDA) models for content-based image retrieval. Image representations are learned in an unsupervised fashion, and each image is modeled as the mixture of topics/object parts depicted in the image. This allows us to put images into subspaces for higher-level reasoning which in turn can be used to find similar images. Different similarity measures based on the described image representation are studied. The presented approach is evaluated on a real world image database consisting of more than 246,000 images and compared to image models based on probabilistic Latent Semantic Analysis (pLSA). Results show the suitabi...

• Type: Conference
• Year: 2007
• Where: CIVR
• Authors: Eva Hörster, Rainer Lienhart, Malcolm Slaney
• Keywords: CIVR 2007, Image, Image Retrieval, Latent Dirichlet Allocation