Compressing Feature Sets with Digital Search Trees

State-of-the-art image retrieval pipelines are based on “bag-of-words” matching. We note that the original order in which features are extracted from the image is discarded in the “bag-of-words” matching pipeline. As a result, a set of features extracted from a query image can be transmitted in any order. A set of m unique features has m! orderings, and if the order of transmission can be discarded, one can reduce the query size by an additional log
₂
(m!) bits. We propose a coding scheme based on Digital Search Trees that reduces size of a set of features by approximately log
₂
(m!) bits. We perform analysis of the scheme, and show how it applies to any set of symbols in which order can be discarded. We illustrate how the scheme can be applied to a set of low bitrate Compressed Histogram of Gradients (CHoG) descriptors.