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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma edit   pdf
url  openurl
  Title Scale coding bag of deep features for human attribute and action recognition Type Journal Article
  Year 2018 Publication Machine Vision and Applications Abbreviated Journal (up) MVAP  
  Volume 29 Issue 1 Pages 55-71  
  Keywords Action recognition; Attribute recognition; Bag of deep features  
  Abstract Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.  
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  Notes LAMP; 600.068; 600.079; 600.106; 600.120 Approved no  
  Call Number Admin @ si @ KWR2018 Serial 3107  
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Author Fei Yang; Yaxing Wang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov edit   pdf
url  openurl
  Title A Novel Framework for Image-to-image Translation and Image Compression Type Journal Article
  Year 2022 Publication Neurocomputing Abbreviated Journal (up) NEUCOM  
  Volume 508 Issue Pages 58-70  
  Keywords  
  Abstract Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely used approach to image processing problems, such as image synthesis, style transfer, and image restoration. At the same time, neural image compression has emerged as a data-driven alternative to traditional coding approaches in visual communications. In this paper, we study the combination of these two paradigms into a joint I2I compression and translation framework, focusing on multi-domain image synthesis. We first propose distributed I2I translation by integrating quantization and entropy coding into an I2I translation framework (i.e. I2Icodec). In practice, the image compression functionality (i.e. autoencoding) is also desirable, requiring to deploy alongside I2Icodec a regular image codec. Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec. Adaptive residual blocks conditioned on the translation/compression mode provide flexible adaptation to the desired functionality. The experiments show promising results in both I2I translation and image compression using a single model.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ YWH2022 Serial 3679  
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal (up) PR  
  Volume 47 Issue 6 Pages 2193-2204  
  Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models  
  Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.  
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  Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no  
  Call Number Admin @ si @ CPR2014b Serial 2470  
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Author Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo edit   pdf
doi  openurl
  Title Leveraging local neighborhood topology for large scale person re-identification Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal (up) PR  
  Volume 47 Issue 12 Pages 3767–3778  
  Keywords Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100  
  Abstract In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data.  
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  Notes LAMP; 601.240; 600.079 Approved no  
  Call Number Admin @ si @ KLB2014a Serial 2522  
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Author Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu edit   pdf
url  openurl
  Title Saliency for fine-grained object recognition in domains with scarce training data Type Journal Article
  Year 2019 Publication Pattern Recognition Abbreviated Journal (up) PR  
  Volume 94 Issue Pages 62-73  
  Keywords  
  Abstract This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.  
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  Notes LAMP; OR; 600.109; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ FGW2019 Serial 3264  
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