Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval

Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which has proven successful in achieving high precision at low recall. Unfortunately, feature detection and quantization are noisy processes and this can result in variation in the particular visual words that appear in different images of the same object, leading to missed results. In the text retrieval literature a standard method for improving performance is query expansion. A number of the highly ranked documents from the original query are reissued as a new query. In this way, additional relevant terms can be added to the query. This is a form of blind relevance feedback and it can fail if ‘outlier’ (false positive) documents are included in the reissued query. In this paper we bring query expansion into the visual domain via two novel contributions. Firstly, strong spatial constraints between the query imag...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Ondrej Chum, James Philbin, Josef Sivic, Michael Isard, Andrew Zisserman
• Keywords: Computer Vision, ICCV 2007, Query Expansion, Query Image, Text-based Query Expansion