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Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Leveraging local neighborhood topology for large scale person re-identification |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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12 |
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3767–3778 |
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Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 |
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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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LAMP; 601.240; 600.079 |
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Admin @ si @ KLB2014a |
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2522 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Improved Recursive Geodesic Distance Computation for Edge Preserving Filter |
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2017 |
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IEEE Transactions on Image Processing |
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TIP |
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26 |
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8 |
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3696 - 3706 |
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Geodesic distance filter; color image filtering; image enhancement |
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All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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LAMP; ISE; 600.120; 600.098; 600.119 |
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Admin @ si @ Moz2017 |
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2921 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |
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Semantic Pyramids for Gender and Action Recognition |
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2014 |
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IEEE Transactions on Image Processing |
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TIP |
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23 |
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8 |
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3633-3645 |
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Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
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1057-7149 |
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CIC; LAMP; 601.160; 600.074; 600.079;MILAB |
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Admin @ si @ KWR2014 |
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2507 |
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Vacit Oguz Yazici; Longlong Yu; Arnau Ramisa; Luis Herranz; Joost Van de Weijer |
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Title |
Main product detection with graph networks for fashion |
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Year |
2024 |
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Multimedia Tools and Applications |
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MTAP |
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83 |
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3215–3231 |
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Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin. |
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LAMP; MACO; 600.147; 600.167; 600.164; 600.161; 600.141; 601.309 |
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Admin @ si @ YYR2024 |
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4017 |
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Author |
Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez |
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Title |
Distributed Learning and Inference with Compressed Images |
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Year |
2021 |
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IEEE Transactions on Image Processing |
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TIP |
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30 |
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3069 - 3083 |
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Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. |
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LAMP; ADAS; 600.120; 600.118 |
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Admin @ si @ KEH2021 |
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3543 |
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