Oscar García-Olalla

Local Oriented Statistics Information Booster (LOSIB) for Texture Classification

Local oriented statistical information booster (LOSIB) is a descriptor enhancer based on the extraction of the gray level differences along several orientations. Specifically, the mean of the differences along particular orientations is considered. In this paper we have carried out some experiments using several classical texture descriptors to show that classification results are better when they are combined with LOSIB, than without it. Both parametric and non-parametric classifiers, Support Vector Machine and k-Nearest Neighbourhoods respectively, were applied to assess this new method. Furthermore, two different texture dataset were evaluated: KTH-Tips-2a and Brodatz32 to prove the robustness of LOSIB. Global descriptors such as WCF4 (Wavelet Co-occurrence Features), that extracts Haralick features from the Wavelet Transform, have been combined with LOSIB obtaining an improvement of 16.94% on KTH and 7.55% on Brodatz when classifying with SVM. Moreover, LOSIB was used together with state-of-the-art local descriptors such as LBP (Local Binary Pattern) and several of its recent variants. Combined with CLBP (Complete LBP), the LOSIB booster results were improved in 5.80% on KTH-Tips 2a and 7.09% on the Brodatz dataset. For all the tested descriptors, we have observed that a higher performance has been achieved, with the two classifiers on both datasets, when using some LOSIB settings.

Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

AbstractA 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.

Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors

The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss. In this work, boar spermatozoa present in gray scale images acquired with a phase-contrast microscope have been classified as acrosome-intact or acrosome-damaged, after using fluorescent images for creating the ground truth. Based on shape prior criteria combined with Otsus thresholding, regional minima and watershed transform, the spermatozoa heads were segmented and registered. One of the main novelties of this proposal is that, unlike what previous works stated, the obtained results show that the contour information of the spermatozoon head is important for improving description and classification. Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date. The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel. Using the biggest acrosome intact-damaged dataset ever created, the early fusion approach followed provides a 0.9913 F-Score, outperforming all previous related works.

Curvelet-based texture description to classify intact and damaged boar spermatozoa

The assessment of boar sperm head images according to their acrosome status is a very important task in the veterinary field. Unfortunately it can only be performed manually, which is slow, non-objective and expensive. It is important to provide companies an automatic and reliable method to perform this task. In this paper a new method which uses texture descriptors based on the Curvelet Transform is proposed. Its performance has been compared with other texture descriptors based on the Wavelet transform, and also with moments based descriptors, as they seem to be successful for this problem. Texture descriptors performed better, and curvelet-based ones achieved the best hit rate (97%) and area under the ROC curve (0.99).

Boar spermatozoa classification using longitudinal and transversal profiles (LTP) descriptor in digital images

A new textural descriptor, named Longitudinal and Transversal Profiles (LTP), has been proposed. This descriptor was used to classify 376 images of dead spermatozoa heads and 472 images of alive ones. The result obtained with this descriptor has been compared with the Pattern spectrum, Flusser, Hu, and a descriptor based on statistical values of the histogram. The features vectors computed have been classified using a back-propagation Neural Network and the kNN (k Nearest Neighbours) algorithm. Classification error obtained with LTP was 30.58% outperforming the other descriptors. The area under the ROC curve (AUC) has also been calculated confirming that the performance of the proposed descriptor is better that of the other texture descriptors.

Evaluation of Different Metrics for Shape Based Image Retrieval Using a New Contour Points Descriptor

In this paper, an image shape retrieval method was evaluated using Euclidean, Intersect, Hamming and Cityblock distances and different kinds of k-nearest neighbours classifiers such as the original kNN, mean distance kNN and Weighted kNN. Shapes were described using a new method based on the description of the contour points, CPDH36R, obtaining better results than with the original CPDH shape descriptor. The efficiency in the retrieval was tested using Kimia99, Kimia25, MPEG7 and MPEG2 datasets obtaining an 84% of success rate in Kimia25, 94% in Kimia99, 91% in MPEG2 and 82% in MPEG7 datasets using our CPDH36R method, cityblock distance and original kNN against the 68%, 91%, 74% and 59% respectively obtained using the original CPDH. The greatest difference between the original method and our proposal can be seen clearly using MPEG2 dataset. Another advantage of our retrieval method, apart from the success rate, is the computational cost which is clearly better than the one achieved with the original Earth Mover Distance classifier used in the CPDH original method.

Evaluation of LBP Variants Using Several Metrics and kNN Classifiers

In this paper, we demonstrate that the Adaptive Local Binary Pattern with oriented Standard deviation ALBPS method outperforms the original local binary pattern LBP as well as some of its most recent variants: Adaptive Local Binary Pattern ALBP, Complete Local Binary Pattern CLBP and Local Binary Pattern Variance LBPV. All the descriptors have been tested using two different dataset, KTH-TIPS 2a, a challenging multiclass dataset for material recognition and a binary sperm dataset for vitality classification. Three variants of the non parametric method of nearest neighbours combined with four metric distances have been used in the retrieval step in order to draw a more decisive conclusion. Best results were achieved when describing the images with ALBPS in both datasets. In regard to the KTH-TIPS 2a, the best performance is obtained using the weighted kNN with a 61.47% of hit rate using ALBPS and Chi Square distance, outperforming the ALBP in 1,07% and the original LBP in 6,76%. In relation to the binary sperm dataset, the best result was obtained with ALBPS and a kNN classifier k=9, reaching a 72.66% of hit rate using the Chi Square metric, outperforming the original LBP in 22,47% and the ALBP in 1,22%. In the latter case, the weighted kNN did not improve the results achieved using just kNN. Taking this results into account, we can determine that ALBPS has more discriminant power for image retrieval than the rest of the tested LBP variants in different image contexts.

Automatic Tampering Detection in Spliced Images with Different Compression Levels

In this paper, we introduce a blind tampering detection method based on JPEG ghosts [3] capable of detecting tampering when it is created by splicing regions with different compression levels in an image. Given an image, a set of re-compressions of that image is generated and used to extract a feature vector to train a Support Vector Machine classifier. We used two different datasets in our experiments. The first one, extracted from Columbia Uncompressed Image Splicing Detection, was used to compare results with other works. Our method outperformed the previous ones when dealing with small tampered regions and similar qualities, offering hit rates above 97% (100% in the case of non-tampered images). With the second dataset, CASIA1 Tampered Image Detection Evaluation Dataset, our method offered a hit rate of 98.71% when discerning between the original and the spliced image, with just a 0.44% of non-tampered images wrongly classified.

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

Evaluation of the State of Cutting Tools According to Its Texture Using LOSIB and LBP Variants

The FRESVIDA project deals with the life assessment of cutting tools working under severe conditions using digital image processing techniques. The description of texture in materials through artificial vision techniques is very useful for this goal. There are several works based on Local Binary Patterns (LBP) and many variants such as Local Binary Pattern Variance (LBPV) or Diamond-LBP Code (DLBPCS) that have proved to be effective when distinguishing materials according to their texture. The Outex dataset comprises images from 24 materials acquired under different illumination conditions, becoming the present reference dataset for texture evaluation. In this work, several descriptors have been extracted based on the LBP from the Outex dataset, as well as their combination with LOSIB (Local Oriented Statistical Information Booster). All of them have been classified with Support Vector Machine (SVM) to assess which one is more useful for the above-mentioned task. In this case, all descriptors achieve a lower performance level combined with LOSIB because Outex is a data set that studies rotation invariances.