Fast correlation matching in large (edge) image databases

Dariu M. Gavrila; Larry S. Davis

doi:10.1117/12.200787

31 January 1995 Fast correlation matching in large (edge) image databases

Dariu M. Gavrila, Larry S. Davis

Proceedings Volume 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; (1995) https://doi.org/10.1117/12.200787
Event: 23 Annual AIPR Workshop: Image and Information Systems: Applications and Opportunities, 1994, Washington, DC, United States

Abstract

Correlation-based matching methods are known to be very expensive when used on large image databases. In this paper, we examine ways of speeding up correlation matching by phase-coded filtering. Phase coded filtering is a technique to combine multiple patterns in one filter by assigning complex weights of unit magnitude to the individual patterns and summing them up in a composite filter. Several of the proposed composite filters are based on this idea, such as the circular harmonic component (CHC) filters and the linear phase coefficient composite (LPCC) filters. We consider the LPCC(1) filter in isolation and examine ways to improve its performance by assigning the complex weights to the individual patterns in a non- random manner so as to maximize the SNR of the filter w.r.t. the individual patterns. In experiments on a database of 100 to 1000 edge images from the aerial domain we examine the trade-off between the speed-up (the number of patterns combined in a filter) and unreliability (the number of resulting false matches) of the composite filter. Results indicate that for binary patterns with point densities of about 0.05 we can safely combine more than 20 patterns in the optimized LPCC(1) filter, which represents a speed-up of an order of a magnitude over the brute force approach of matching the individual patterns.

Citation Download Citation

Dariu M. Gavrila and Larry S. Davis "Fast correlation matching in large (edge) image databases", Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); https://doi.org/10.1117/12.200787

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