Automatic cattle identification using graph matching based on local invariant features

Cattle muzzle classification can be considered as a biometric identifier important to animal traceability systems to ensure the integrity of the food chain. This paper presents a muzzle-based classification system that combines local invariant features with graph matching. The proposed approach consists of three phases; namely feature extraction, graph matching, and matching refinement. The experimental results showed that our approach is superior than existing works as ours achieves an all correct identification for the tested images. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angles.info:eu-repo/semantics/publishedVersio

Learning with Biased Complementary Labels

In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that observations do \textbf{not} belong to. Let $Y$ and $\bar{Y}$ be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities $P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall i\neq j$, are identical, which is not true in practice because humans are biased toward their own experience. For example, as shown in Figure 1, if an annotator is more familiar with monkeys than prairie dogs when providing complementary labels for meerkats, she is more likely to employ "monkey" as a complementary label. We therefore reason that the transition probabilities will be different. In this paper, we propose a framework that contributes three main innovations to learning with \textbf{biased} complementary labels: (1) It estimates transition probabilities with no bias. (2) It provides a general method to modify traditional loss functions and extends standard deep neural network classifiers to learn with biased complementary labels. (3) It theoretically ensures that the classifier learned with complementary labels converges to the optimal one learned with true labels. Comprehensive experiments on several benchmark datasets validate the superiority of our method to current state-of-the-art methods.Comment: ECCV 2018 Ora

Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean

Experimental and modelling work suggests a strong dependence of olive flowering date on spring temperatures. Since airborne pollen concentrations reflect the flowering phenology of olive populations within a radius of 50 km, they may be a sensitive regional indicator of climatic warming. We assessed this potential sensitivity with phenology models fitted to flowering dates inferred from maximum airborne pollen data. Of four models tested, a thermal time model gave the best fit for Montpellier, France, and was the most effective at the regional scale, providing reasonable predictions for 10 sites in the western Mediterranean. This model was forced with replicated future temperature simulations for the western Mediterranean from a coupled ocean-atmosphere general circulation model (GCM). The GCM temperatures rose by 4·5 °C between 1990 and 2099 with a 1% per year increase in greenhouse gases, and modelled flowering date advanced at a rate of 6·2 d per °C. The results indicated that this long-term regional trend in phenology might be statistically significant as early as 2030, but with marked spatial variation in magnitude, with the calculated flowering date between the 1990s and 2030s advancing by 3–23 d. Future monitoring of airborne olive pollen may therefore provide an early biological indicator of climatic warming in the Mediterranean

Removal of pharmaceuticals in WWTP effluents by ozone and hydrogen peroxide

Ozonation to achieve removal of pharmaceuticals from wastewater effluents, with pH values in the upper and lower regions of the typical range for Swedish wastewater, was investigated. The main aim was to study the effects of varying pH values (6.0 and 8.0), and if small additions of H2O2 prior to ozone treatment could improve the removal and lower the reaction time. The effluents studied differed in their chemical characteristics, particularly in terms of alkalinity (65.3-427 mg center dot l(-1) HCO3-), COD (18.2-41.8 mg center dot l(-1)), DOC (6.9-12.5 mg center dot l(-1)), ammonium content (0.02-3.6 mg center dot l(-1)) and specific UV absorbance (1.78-2.76 l center dot mg(-1)center dot m(-1)). As expected, lower ozone decomposition rates were observed in the effluents at pH 6.0 compared to pH 8.0. When pH 8.0 effluents were ozonated, a higher degree of pharmaceutical removal occurred in the effluent with low specific UV absorbance. For pH 6.0 effluents, the removal of pharmaceuticals was most efficient in the effluent with the lowest organic content. The addition of H2O2 had no significant effect on the quantitative removal of pharmaceuticals but enhanced the ozone decomposition rate. Thus, H2O2 addition increased the reaction rate. In practice, this will mean that the reactor volume needed for the ozonation of wastewater effluents can be reduced

Graph similarity through entropic manifold alignment

In this paper we decouple the problem of measuring graph similarity into two sequential steps. The first step is the linearization of the quadratic assignment problem (QAP) in a low-dimensional space, given by the embedding trick. The second step is the evaluation of an information-theoretic distributional measure, which relies on deformable manifold alignment. The proposed measure is a normalized conditional entropy, which induces a positive definite kernel when symmetrized. We use bypass entropy estimation methods to compute an approximation of the normalized conditional entropy. Our approach, which is purely topological (i.e., it does not rely on node or edge attributes although it can potentially accommodate them as additional sources of information) is competitive with state-of-the-art graph matching algorithms as sources of correspondence-based graph similarity, but its complexity is linear instead of cubic (although the complexity of the similarity measure is quadratic). We also determine that the best embedding strategy for graph similarity is provided by commute time embedding, and we conjecture that this is related to its inversibility property, since the inverse of the embeddings obtained using our method can be used as a generative sampler of graph structure.The work of the first and third authors was supported by the projects TIN2012-32839 and TIN2015-69077-P of the Spanish Government. The work of the second author was supported by a Royal Society Wolfson Research Merit Award