A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

A comparison of techniques for robust gender recognition

Reprinted, with permission, from [Rojas Bello, R.N., Lago Fernández, L.F., Martínez Muñoz, G., y Sánchez Montañés, M.A., A comparision of techniques for robust gender recognition, IEEE International Conference on Image Processing, ICIP 2011]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the Universidad Autónoma de Madrid's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Proceedings of 2011 18th IEEE International Conference on Image Processing (ICIP), 11-14 Sept. 2011, BrusselsAutomatic gender classification of face images is an area of growing interest with multiple applications. Appropriate classifiers should be robust against variations such as illumination, scale and orientation that occur in real world applications. This can be achieved by normalizing the images in order to reduce those variations (alignment, re-scaling, histogram-equalization, etc.), or by extracting features from the original images which are invariant respect to those variations. In this work we perform a robust comparison of eight different classifiers across 100 random partitions of a set of frontal face images. Four of them are state-of-the-art methods in automatic gender classification that use image normalization (SVMs, Neural Networks, ADABOOST and PCA+LDA). The other four strategies use invariant features extracted by SIFT (BOW, Evidence Random Trees, NBNN and Voted Nearest-Neighbor). The best strategies are SVM using normalized images and NBNN, the latter having the advantage that no strong image pre-processing is needed.This work has been supported by CDTI (project INTEGRA) and DGUICAM/UAM (project CCG10-UAM/TIC-5864

SPECIES IDENTIFICATION FOR AQUATIC BIOMONITORING USING DEEP RESIDUAL CNN AND TRANSFER LEARNING

Aquatic insects and other benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, an expensive and time-consuming process of species identification represents one of the key obstacles for reliable biomonitoring of aquatic ecosystems. In this paper, we proposed a deep learning (DL) based method for species identification that we evaluated on several available public datasets (FIN-Benthic, STONEFLY9, and EPT29) along with our Chironomidae dataset (CHIRO10). The proposed method relies on three DL techniques used to improve robustness when training is done on a relatively small dataset: transfer learning, data augmentation, and feature dropout. We applied transfer learning by employing ResNet-50 deep convolutional neural network (CNN) pretrained on ImageNet 2012 dataset. The results show significant improvement compared to original contributions and confirms that there is a considerable gain when there are multiple images per specimen