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Pedro Martins; Paulo Carvalho; Carlo Gatta |
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On the completeness of feature-driven maximally stable extremal regions |
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2016 |
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Pattern Recognition Letters |
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PRL |
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74 |
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9-16 |
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Local features; Completeness; Maximally Stable Extremal Regions |
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By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
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Elsevier B.V. |
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0167-8655 |
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LAMP;MILAB; |
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Admin @ si @ MCG2016 |
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2748 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Title |
Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
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2016 |
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IEEE Transactions on Affective Computing |
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TAC |
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9 |
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2 |
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161-175 |
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Mirroring; Nodding; Competence; Perception; Wearable Technology |
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Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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LAMP; 600.072; |
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Admin @ si @ MTR2016 |
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2826 |
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Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Local pyramidal descriptors for image recognition |
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2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
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5 |
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1033 - 1040 |
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Object categorization; local features; kernel methods |
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In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain
further improvement.We achieve state-of-the-art results on Caltech-101
(80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
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0162-8828 |
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LAMP; 600.079 |
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Admin @ si @ SSB2014 |
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2524 |
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Author |
Kai Wang; Joost Van de Weijer; Luis Herranz |
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Title |
ACAE-REMIND for online continual learning with compressed feature replay |
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2021 |
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Pattern Recognition Letters |
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PRL |
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150 |
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122-129 |
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Keywords |
online continual learning; autoencoders; vector quantization |
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Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. |
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LAMP; 600.147; 601.379; 600.120; 600.141 |
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Admin @ si @ WWH2021 |
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3575 |
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Author |
Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores |
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Title |
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation |
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2020 |
Publication |
Engineering Applications of Artificial Intelligence |
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EAAI |
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87 |
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103309 |
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Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance |
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Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance. |
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LAMP; 600.109; 600.120 |
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Admin @ si @ RRM2020 |
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3401 |
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