Covering C4⨁e by the same label

In pattern recognition applications, it is useful to represent objects by attributed graphs considering their structural properties. Besides, some graph matching problems need a Common Labelling between vertices of a set of graphs. Computing this Common Labelling is an NP-complete problem. State of the-art algorithms are composed by two steps: in the first, they compute all pairwise labellings among the graphs and in the second, they combine this information to obtain a Common Labelling. The drawback of these methods is that global information is only considered in the second step. To solve this problem, by reducing the Common Labelling problem to the quadratic assignment one, all graphs nodes are labelled to a virtual structure whereby the Common Labeling is generated using global information. We tested the algorithm on both real-world and synthetic data. We show that the algorithm offers better performance than a reference method with same computational cost.

The computation of a common labelling of a set of graphs is required to find a representative of a given graph set. Although this is a NP-problem, practical methods exist to obtain a sub-optimal common labelling in polynomial time. We consider the graphs in the set have a Gaussian distortion, and so, the average labelling is the one that obtains the best common labelling. In this paper, we present two new algorithms to find a common labelling between a set of attributed graphs, which are based on a probabilistic framework. They have two main advantages. From the theoretical point of view, no additional nodes are artificial introduced to obtain the common labelling, and so, the structure of the graphs in the set is kept unaltered. From the practical point of view, results show that the presented algorithms outperform state-of-the-art algorithms.KeywordsSynthetic DatasetProbabilistic FrameworkAttribute GraphBinary AttributeCommon LabellingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Prediction of common labels for universal domain adaptation

(097–100) Proposals to clarify Articles 8.2 and 8.3 and their footnotes with regard to specimens, gatherings and duplicates

In pattern recognition applications, with the aim of increasing efficiency, it is useful to represent the elements by attributed graphs (which consider their structural properties). Under this structural representation of the elements some graph matching problems need a common labeling between the vertices of a set of graphs. Computing this common labeling is a NP-Complete problem. Nevertheless, some methodologies have been presented which obtain a sub-optimal solution in polynomial time. The drawback of these methods is that they rely on pairwise labeling computations, causing the methodologies not to consider the global information during the entire process. To solve this problem, we present a methodology which generates the common labeling by matching all graph nodes to a virtual node set. The method has been tested using three independent datasets, one synthetic and two real. Experimental results show that the presented method obtains better performance than the most popular common labeling algorithm with the same computational cost.

Against the problem of mining the potential relationship among the micro blog users, this paper drawing lessons from the thought of coupling analysis and using vector space model to build users labels vector space based on the users’ common labels. This paper then calculate the users label similarity to measure potential relationships among the users and use the analysis method of cohesive subgroup to mine micro blog user groups with higher cohesion in potential relationship network. Finally, the empirical analysis combined with sample data was conducted to verify the feasibility and reasonableness of the methods described herein.

Models and algorithms for computing the common labelling of a set of attributed graphs

In structural pattern recognition, given a set of graphs, the computation of a Generalized Median Graph is a well known problem. Some methods approach the problem by assuming a relation between the Generalized Median Graph and the Common Labelling problem. However, this relation has still not been formally proved. In this paper, we analyse such relation between both problems. The main result proves that the cost of the common labelling upper-bounds the cost of the median with respect to the given set. In addition, we show that the two problems are equivalent in some cases.KeywordsCost FunctionWeighted GraphVirtual NodeGraph MatchPermutation MatriceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

‘Fundamentalism’ Christian and Islamic

Many tasks in computer vision involve assigning a label (such as disparity) to every pixel. These tasks can be formulated as energy minimization problems. In this paper, we consider a natural class of energy functions that permits discontinuities. Computingthe exact minimum is NP-hard. We have developed a new approximation algorithm based on graph cuts. The solution it generates is guaranteed to be within a factor of 2 of the energy function’s global minimum. Our method produces a local minimum with respect to a certain move space. In this move space, a single move is allowed to switch an arbitrary subset of pixels to one common label. If this common label is á then such a move expands the domain of á in the image. At each iteration our algorithm efficiently chooses the expansion move that gives the largest decrease in the energy. We apply our method to the stereo matching problem, and obtain promisingex perimental results. Empirically, the new technique outperforms our previous algorithm [6] both in terms of runningti me and output quality.KeywordsSimulated AnnealingEnergy FunctionGlobal MinimumNeighboring PixelPotts EnergyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As a way to facilitate image categorization and retrieval, automatic image annotation has received much research attention. Traditional web image annotation methods often estimate the label relevance to image by the most common labels' frequency derived from its nearest neighbors, and neglect the relevance of the assigned label set as a whole. We propose in this paper a novel search based image annotation method by learning label set relevance, which aims at annotating large scale image collections in real environment. "Label set"-to-image relevance and label set correlation are formulated into a joint framework. Measures that can estimate the label set relevance are designed. The assigned label set provide a more precise description of the image's content. To reduce the complexity, a heuristic algorithm is introduced to annotate image accurately and efficiently in large scale web image set. Experiments on real world web dataset demonstrate the general applicability of our algorithm in web image annotation. In comparison to state-of-the-art, the proposed method achieves excellent performance.

In structural pattern recognition, given a set of graphs, the computation of a Generalized Median Graph is a well known problem. Some methods approach the problem by assuming a relation between the Generalized Median Graph and the Common Labelling problem. However, this relation has still not been formally proved. In this paper, we analyse such relation between both problems. The main result proves that the cost of the common labelling upper-bounds the cost of the median with respect to the given set. In addition, we show that the two problems are equivalent in some cases.


 Protected Designation of Origin (PDO) is one of the EU tools for rural development. Most of the literature on this subject is focused on premium prices and consumers’ willingness to pay for local products, since PDO and other labels aim to provide premium incomes for farmers. Our assumption is that PDO drives unexpected changes of farming styles not only related to processing or market strategies but also related to local resources using and to stablishing of different approach to agriculture and food production. We analyzed the PDO Queso Palmero (La Palma cheese) as a case of a dual label system (brand–certification common label) because it gives us the opportunity to compare farmers involved in a PDO scheme with farmers who works outside such systems. We conclude that private brands are more important than common label certification in price formation, but both are complementary, since PDO reinforces farmers’ efforts to improve quality. Beyond premium price, PDO also drives a radical change in farm structures, since it reconnects products to local resources (grazing vs intensification) and redesigns relationships with markets (shortening and diversifying chains and widening product offer). This change is characterized by implementation of new farming strategies in the context of PDO structure that coexist with classical farming strategies closer to intensification, not only in terms of productivity but also in terms of decoupling from local resources and productive and market specialization. Therefore, PDO is a powerful tool for rural development in a wide sense (resilience, empowerment, local capacity and network formation among others) far beyond its narrow remit of promoting economic growth (local or regional). Therefore, the coupling with local resources and the strength of local network and relationships as source of resilience, knowledge and capabilities improvement, have to be included in performance assessment of GIs in order to broaden the appraisal of role in regional development.
 Highlights
 
 PDO as institution is a powerful tool of farm transformation not only a protection structure of collective heritage or asset.
 Private brand effect on price is larger than common label effect (PDO label).
 PDO as institution leads radical changes of goat production systems from more production – oriented toward more market – oriented styles.
 Market chains and product diversification, focusing on quality, concern about consumers and coupling with local resources are distinctive features of farms involved in PDO.

Cross-modal retrieval has attracted much attention in recent years due to its widespread applications. In this area, how to capture and correlate heterogeneous features originating from different modalities remains a challenge. However, most existing methods dealing with cross-modal learning only focus on learning relevant features shared by two distinct feature spaces, therefore overlooking discriminative feature information of them. To remedy this issue and explicitly capture discriminative feature information, we propose a novel cross-modal retrieval approach based on discriminative dictionary learning that is augmented with common label alignment. Concretely, a discriminative dictionary is first learned to account for each modality, which boosts not only the discriminating capability of intra-modality data from different classes but also the relevance of inter-modality data in the same class. Subsequently, all the resulting sparse codes are simultaneously mapped to a common label space, where the cross-modal data samples are characterized and associated. Also in the label space, the discriminativeness and relevance of the considered cross-modal data can be further strengthened by enforcing a common label alignment. Finally, cross-modal retrieval is performed over the common label space. Experiments conducted on two public cross-modal datasets show that the proposed approach outperforms several state-of-the-art methods in term of retrieval accuracy.

Anguillid eels