Selection of relevant information to improve Image Classification using Bag of Visual Words

One of the main challenges in computer vision is image classification. Nowadays the number of images increases exponentially every day; therefore, it is important to classify them in a reliable way.The conventional image classification pipeline usually consists on extracting local image features, encoding them as a feature vector and classify them using a previously created model. With regards to feature codification, the Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have achieved quite good results and have become the state of the art methods.The process as mentioned above is not perfect and computers, as well as humans, may make mistakes in any of the steps, causing a performance drop in classification. Some of the primary sources of error on large-scale image classification are the presence of multiple objects in the image, small or very thin objects, incorrect annotations or fine-grained recognition tasks among others.Based on those problems and the steps of a typical image classification pipeline, the motivation of this PhD thesis was to provide some guidelines to improve the quality of the extracted features to obtain better classification results. The contributions of the PhD thesis demonstrated how a good feature selection can contribute to improving the fine-grained classification, and that there would even be no need to have a big training data set to learn the key features of each class and to predict with good results

Selection of relevant information to improve Image Classification using Bag of Visual Words

One of the main challenges in computer vision is image classification. Nowadays the number of images increases exponentially every day; therefore, it is important to classify them in a reliable way.The conventional image classification pipeline usually consists on extracting local image features, encoding them as a feature vector and classify them using a previously created model. With regards to feature codification, the Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have achieved quite good results and have become the state of the art methods.The process as mentioned above is not perfect and computers, as well as humans, may make mistakes in any of the steps, causing a performance drop in classification. Some of the primary sources of error on large-scale image classification are the presence of multiple objects in the image, small or very thin objects, incorrect annotations or fine-grained recognition tasks among others.Based on those problems and the steps of a typical image classification pipeline, the motivation of this PhD thesis was to provide some guidelines to improve the quality of the extracted features to obtain better classification results. The contributions of the PhD thesis demonstrated how a good feature selection can contribute to improving the fine-grained classification, and that there would even be no need to have a big training data set to learn the key features of each class and to predict with good results

A new species of Hexacladia Ashmead (Hymenoptera, Encyrtidae) and new record of Hexacladia smithii Ashmead as parasitoids of Dichelops furcatus (Fabricius) (Hemiptera, Pentatomidae) in Argentina

Pentatomid adults of the species Dichelops furcatus (F.), collected on stubble of soybean, Glycine max (Linnaeus) Merril, in Santa Fe province of Argentina, were found parasitized by two encyrtid wasp species (Hymenoptera: Encyrtidae). One of the encyrtids is described as Hexacladia dichelopsis Torréns & Fidalgo, sp. n., from both sexes, and the other species H. smithii Ashmead, is recorded for the first time from D. furcatus in Argentina. Both species are gregarious endoparasitoids which carry out the whole development (larval and pupal) in their living hosts; they emerge as imagoes, by cutting their way out through the dorsal wall of the abdomen. Including the newly described H. dichelopsis, seven species of the genus are recorded from South America, and an identification key to separate them is presented. Copyright Javier Torréns et al.Fil: Torrens, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Fidalgo, Alberto Antonio P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Fernández, Celina Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Punschke, Eduardo. Universidad Nacional de Rosario; Argentin

Stellate Ulceration in a Nonuremic Patient

info:eu-repo/semantics/publishedVersio

Estimating product efficiency through a hedonic pricing best practice frontier

A complex critical decision in marketing and economics is pricing. Finding the right price for a product requires careful assessment of the product attributes. Product efficiency evaluation establishes the relative appeal of a product, when compared with the observable attributes and prices of competing products. The main contribution of this paper is combining hedonic pricing with frontier analysis to estimate product efficiency, which is a novel approach. We apply this method to the running shoes market. We find four attributes as main drivers of price: Stability, Cushioning, Flexibility and Response. The model also identifies overpriced products and predicts the price reductions needed in order to be comparatively competitive, a prerequisite for overall business performance. Our results show the dynamics of price adjustments in the market. Overpriced products adjust prices down quickly gaining comparative appeal. Another interesting finding is that product efficiency strongly correlates with the evaluations made by independent experts. JEL classification: M31, L11, C13, Keywords: Hedonic pricing, Stochastic frontier analysis, Product efficiency, Running shoe