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EnglishThis paper purposes a new method for selecting the most discriminant rotation invariant patterns in local binary patterns and local ternary patterns. Our experiments show that a selection based on variance performs better than the recently proposed method of using dominant local binary patterns (DLBP). Our method uses a random subspace of patterns with higher variance. Features are transformed using Neighborhood Preserving Embedding (NPE) and then used to train a support vector machine. Moreover, we extend DLBP with local ternary patterns (DLTP) and examine methods for building a supervised random subspace of classifiers where each bin of the histogram has a probability of belonging to a given subspace according to its occurrence frequencies. We compare several texture descriptors and show that the random subspace ensemble based on NPE features outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using five benchmark databases.
| Titolo: | A Study for Selecting the Best Performing Rotation Invariant Patterns in Local Binary/Ternary Patterns |
| Autore/i: | NANNI, LORIS; S. Brahnam; LUMINI, ALESSANDRA |
| Autore/i Unibo: | |
| Anno: | 2010 |
| Titolo del libro: | proceedings International Conference on Image Processing, Computer Vision & Pattern Recognition (IPCV'10) |
| Pagina iniziale: | 369 |
| Pagina finale: | 375 |
| Abstract: | This paper purposes a new method for selecting the most discriminant rotation invariant patterns in local binary patterns and local ternary patterns. Our experiments show that a selection based on variance performs better than the recently proposed method of using dominant local binary patterns (DLBP). Our method uses a random subspace of patterns with higher variance. Features are transformed using Neighborhood Preserving Embedding (NPE) and then used to train a support vector machine. Moreover, we extend DLBP with local ternary patterns (DLTP) and examine methods for building a supervised random subspace of classifiers where each bin of the histogram has a probability of belonging to a given subspace according to its occurrence frequencies. We compare several texture descriptors and show that the random subspace ensemble based on NPE features outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using five benchmark databases. |
| Data prodotto definitivo in UGOV: | 4-feb-2011 |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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