Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions

Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper we try to overcome this difficulty by presenting a multi-utility learning framework for structured prediction that can learn from training instances with different forms of supervision. We propose a unified technique for inferring the loss functions most suitable for quantifying the consistency of solutions with the given weak annotation. We demonstrate the effectiveness of our framework on the challenging semantic image segmentation problem for which a wide variety of annotations can be used. For instance, the popular training datasets for semantic segmentation are composed of images with hard-to-generate full pixel labellings, as well as images with easy-to-obtain weak annotations, such as bounding boxes around objects, or image-level labels that specify which object categories are present in an image. Experimental evaluation shows that the use of annotation-specific loss functions dramatically improves segmentation accuracy compared to the baseline system where only one type of weak annotation is used

Model and Feature Selection in Hidden Conditional Random Fields with Group Regularization

Proceedings of: 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). Salamanca, September 11-13, 2013.Sequence classification is an important problem in computer vision, speech analysis or computational biology. This paper presents a new training strategy for the Hidden Conditional Random Field sequence classifier incorporating model and feature selection. The standard Lasso regularization employed in the estimation of model parameters is replaced by overlapping group-L1 regularization. Depending on the configuration of the overlapping groups, model selection, feature selection,or both are performed. The sequence classifiers trained in this way have better predictive performance. The application of the proposed method in a human action recognition task confirms that fact.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)Publicad

Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification

Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad

Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications

<p>Abstract</p> <p>Background</p> <p>Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied.</p> <p>Results</p> <p>We introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) as an extension of Hidden Conditional Random Fields (HCRFs). GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows <it>regular grammar </it>rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins.</p> <p>Conclusion</p> <p>We show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications.</p> <p>Availability</p> <p>GRHCRF software is available under GPLv3 licence at the website</p> <p><url>http://www.biocomp.unibo.it/~savojard/biocrf-0.9.tar.gz.</url></p

Weakly Supervised Localization and Learning with Generic Knowledge

ISSN:0920-5691ISSN:1573-140

Verksamhetsmodell för klinisk specialistsjukskötare inom samjour vid Vasa centralsjukhus : - en kvalitativ studie

Syftet med studien var att utveckla en verksamhetsmodell för klinisk specialistsjukskötare inom samjour vid Vasa centralsjukhus. Frågeställningen för studien var: Hur skall verksamhetsmodellen utformas för en klinisk specialistsjukskötare? Vilka ansvarsområden kan en klinisk specialistsjukskötare inneha vid samjouren? Vilka arbetsuppgifter kan en klinisk specialistsjukskötare ha inom samjouren vid Vasa centralsjukhus? Metoden som användes var aktionsforskning med kvalitativ ansats. Datainsamlingsmetoden var enkät med öppna frågor till klinisk specialistsjukskötare i expertfunktion inom specialsjukvården och inom primärhälsovården vid olika sjukvårdsdistrikt i Finland. Data analyserades med innehållsanalys. För att utvärdera verksamhetsmodellen användes enkätsvaren och forskningar. Utgående från svaren bearbetades verksamhetsmodellen till det slutliga formatet. Resultatet av studien visar att klinisk specialistsjukskötaren arbetar självständigt, innehar en fördjupad medicinsk kompetens och har ett ansvar för att patienten skall få en evidensbaserad vård. Resultatet i studien visar också att om en klinisk specialistsjukskötare implementeras inom organisationen så utvecklas verksamhetsmodeller enligt de internationella kraven. Verksamhetsmodellens tyngdpunkt sätts på en god och trygg vård till patienterna. Målgruppen för klinisk specialistsjukskötare i denhär studien är främst patienter som besöker samjouren vid Vasa centralsjukhus.The aim of the study was to develop a case of management model for a clinical nurse specialist in primary health care at Vaasa Central Hospital. The research question was the following: How will the operational model be designed for a clinical nurse specialist? What responsibilities can be given to clinical nurse specialists in primary health care? What duties can clinical nurse specialists have within primary health care at Vaasa Central Hospital? The method used was action research with a qualitative approach. The instrument was a questionnaire with open-ended questions for nurses performing expert duties within specialist health care and primary health care, in various medical care districts in Finland. The data was analysed by means of content analysis. In order to evaluate the management model, the questionnaire responses and previous research were used, and based on the responses the management model was developed into its final format. The results of the study show that the clinical nurse specialist works independently, possesses in-depth medical skills and has a responsibility to ensure that the patient receives evidence-based care. The results of the study also show that the clinical nurse specialist is implemented within the organization to develop management models with the international requirements. The emphasis of the management model is good and safe care for patients. The target group for the clinical nurse specialist in this study is primarily patients who visit the primary health care at Vaasa Central Hospital

A Discriminative Latent Model of Object Classes and Attributes

Abstract. We present a discriminatively trained model for joint modelling of object class labels (e.g. “person”, “dog”, “chair”, etc.) and their visual attributes (e.g. “has head”, “furry”, “metal”, etc.). We treat attributes of an object as latent variables in our model and capture the correlations among attributes using an undirected graphical model built from training data. The advantage of our model is that it allows us to infer object class labels using the information of both the test image itself and its (latent) attributes. Our model unifies object class prediction and attribute prediction in a principled framework. It is also flexible enough to deal with different performance measurements. Our experimental results provide quantitative evidence that attributes can improve object naming.