Abstract
A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation of the images, and hence, it has been called adaptive local binary pattern with oriented standard deviation (ALBPS). Regarding semen vitality assessment, ALBPS outperformed previous literature works with an 81.88% accuracy and also yielded higher hit rates than the LBP and ALBP baseline methods. Concerning the global description of the images, several classical texture algorithms were tested and a descriptor based on wavelet transform and Haralick feature extraction (wavelet concurrent feature 13 (WCF13)) obtained the best results. Both local and global descriptors were combined, and the classification was carried out with a support vector machine. Two data sets have been evaluated: textures under varying illumination, pose and scale (KTH-TIPS) 2a data set and a second spermatozoa boar data set used to distinguish between dead or alive sperm heads. Therefore, our proposal is novel in three ways. First, a new local feature extraction method ALBPS is introduced. Second, a hybrid method combining the proposed local ALBPS and a global descriptor is presented, outperforming our first approach and all other methods evaluated for this problem. Third, texture classification accuracy is greatly improved with the two former texture descriptors presented. F score and accuracy values were computed in order to measure the performance. The best overall result was yielded by combining ALBPS with WCF13, reaching an F score = 0.886 and an accuracy of 85.63% in the spermatozoa data set and an 84.45% of hit rate in the KTH-TIPS 2a.
Highlights
Texture analysis is a basic vision problem which has been thoroughly studied in the last decades [1]
It is possible to see the F score, precision, recall and accuracy achieved with these descriptors, whereas in Figure 6, the F score and accuracy results and how they are directly related are shown graphically. As it can be noticed, using wavelet concurrent feature 13 (WCF13), the performance improved compared to the rest of the global descriptors, yielding both the best F score value and the best accuracy
We used the local texture descriptors Local binary patterns (LBP) and the adaptive version adaptive local binary pattern (ALBP) proposed by Guo et al [11]
Summary
Texture analysis is a basic vision problem which has been thoroughly studied in the last decades [1]. Its numerous applications vary from object recognition to content-based image retrieval. The first works in texture classification focus on the statistical analysis of images. Some representative methods [2,3,4] rely on texture features determined from the grey level co-occurrence matrix (GLCM) by Haralick [5], while others are based on Gabor filter bank responses, one of the most prevailing filters [6]. Local binary patterns (LBP), proposed by Ojala et al [7], are a recent method commonly used to describe texture. Due to its computational simplicity and discriminative power, LBP has become a popular approach in several applications such as texture classification and segmentation, face recognition or visual inspection, specially in challenging real-time settings. Many variants have been proposed in order to improve its performance in terms of, for example, rotation invariant [8], robustness on flat images [9] or color invariance [10]
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