Algorithms for defining visual regions-of-interest: comparison with eye fixations

Claudio M. Privitera, Lawrence Stark

IEEE Transactions on Pattern Analysis and Machine Intelligence · 2000 · 529 citations · 22 references

Concepts

TL;DR

Machine‑vision tasks such as compression, database querying, and image understanding rely on selecting representative image subsets called regions‑of‑interest (ROIs), while human perception directs eye movements to similar human‑identified ROIs. The study introduces a methodology to automatically detect algorithmically defined ROIs and compares them with human‑identified ROIs to evaluate and select bottom‑up, context‑free algorithms. The approach employs various image‑processing algorithms followed by clustering to generate algorithmically detected ROIs. The authors demonstrate that the proposed methodology can automatically identify ROIs using image‑processing algorithms and clustering, and they discuss its application.

Abstract

Many machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest (ROIs). We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (algorithmically detected ROIs) using different image processing algorithms (IPAs), and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (human identified ROIs). In the paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed.

References

22