The asymptotic tradeoff between the number of distinguishable objects and the necessary
storage space (or equivalently, the search complexity) in an identification system is …

We consider approaches for similarity search in correlated, high-dimensional data sets,
which are derived within a clustering framework. We note that indexing by “vector …

Recently, various patch-based approaches have emerged for high and very high resolution
multispectral image classification and indexing. This comes as a consequence of the most …

We study a two-stage identification problem with pre-processing to enable efficient data
retrieval and reconstruction. In the enrollment phase, users' data are stored into the …

The problem of performing similarity queries on compressed data is considered. We focus
on the quadratic similarity measure, and study the fundamental tradeoff between …

The problem of private information¿ leakage¿(inadvertently or by malicious design) from the
myriad large centralized searchable data repositories drives the need for an analytical …

We study two-stage search procedures for biometric identification systems in an information-
theoretical setting. Our main conclusion is that clustering based on vector-quantization …

Image-driven data mining methods are described for image content segmentation,
classification, and attribution, where each pixel location of an image-under-analysis is the …

Ensuring the usefulness of electronic data sources while providing necessary privacy
guarantees is an important unsolved problem. This problem drives the need for an …

In an information-theoretic framework, we introduce a two-stage decoding scheme capable
of achieving identification capacity to address search and memory complexities in large …