Combining textural descriptors for forest species recognition

• Published in: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society pp. 1483 - 1488
• Main Authors: Martins, J. G., Oliveira, L. S., Sabourin, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Support vector machines; Vectors
• ISBN: 9781467324199; 1467324191
• ISSN: 1553-572X
• DOI: 10.1109/IECON.2012.6388523

In this work we assess the recently introduced Local Phase Quantization (LPQ) as textural descriptor for the problem of forest species recognition. LPQ is based on quantizing the Fourier transform phase in local neighborhoods and the phase can be shown to be a blur invariant property under certain commonly fulfilled conditions. We show through a series of comprehensive experiments that LPQ surpasses the results achieved by the widely used Local Binary Patterns (LPB) and its variants. Our experiments also show, though, that the results can be further improved by combining both LPB and LPQ. In this sense, several different combination strategies were tried out. Using a SVM classifiers, the combination of LPB and LPQ brought an improvement of about 7 percentage points on a database composed by 2,240 microscopic images extracted from 112 different forest species.