The computation of a continuous generative model to describe a finite sample of an infinite metric space can prove challenging and lead to erroneous hypothesis, particularly in high-dimensional spaces. In this paper, we follow a different route and define the Hubness Half Space Partitioning graph (HubHSP graph). By constructing this spanning graph over the dataset, we can capture both the geometrical and statistical properties of the data without resorting to any continuity assumption. Leveraging the classical graph-theoretic apparatus, the HubHSP graph facilitates critical operations, including the creation of a representative sample of the original dataset, without relying on density estimation. This representative subsample is essential for a range of operations, including indexing, visualization, and machine learning tasks such as clustering or inductive learning. With the HubHSP graph, we can bypass the limitations of traditional methods and obtain a holistic understanding of our dataset’s properties, enabling us to unlock its full potential.

Indexing web-scale multimedia is only possible by distributing storage and computing efforts. Existing large-scale content-based indexing services mostly do not offer interactive relevance feedback. Here, we propose a running demonstrator of our Cross-Modal Search Engine (CMSE) implementing a query-by-example search strategy with relevance feedback and distributed over a cluster of 20 Dual core machines using MPI. We present the performance gain in terms of interactivity (search time) using a part of the Image-Net collection containing more than one million images as base example.

Multi-view clustering is an important problem in information retrieval due to the abundance of data offering many perspectives and generating multi-view representations. We investigate in this short note a late fusion approach for multi-view clustering based on the latent modeling of cluster-cluster relationships. We derive a probabilistic multi-view clustering model outperforming an early-fusion approach based on multi-view feature correlation analysis.

Abstract In this article we address the problem of benchmarking image browsers. Image browsers are systems that help the user in finding an image from scratch, as opposed to query by example (QBE), where an example image is needed. The existence of different search paradigms for image browsers makes it difficult to compare image browsers. Currently, the only admissible way of evaluation is by conducting large‐scale user studies. This makes it difficult to use such an evaluation as a tool for improving browsing systems. As a solution, we propose an automatic image browser benchmark that uses structured text annotation of the image collection for the simulation of the user's needs. We apply such a benchmark on an example system.