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Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana |
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Title |
Saliency from High-Level Semantic Image Features |
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2020 |
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SN Computer Science |
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SN |
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1 |
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4 |
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1-12 |
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Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). |
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LAMP; 600.120; 600.109; 600.106 |
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Admin @ si @ AWD2020 |
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3503 |
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Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana |
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Title |
Context Proposals for Saliency Detection |
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Journal Article |
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2018 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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174 |
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1-11 |
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One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions
exist which potentially can all be salient. One way to prevent an exhaustive
search over all object regions is by using object proposal algorithms. These
return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated.
In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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LAMP; 600.109; 600.109; 600.120 |
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Admin @ si @ AWD2018 |
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3241 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Embedding new observations via sparse-coding for non-linear manifold learning |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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1 |
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480-492 |
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Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. |
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LAMP; |
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Admin @ si @ RaD2013b |
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2316 |
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Author |
Carlo Gatta; Francesco Ciompi |
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Title |
Stacked Sequential Scale-Space Taylor Context |
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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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8 |
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1694-1700 |
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We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. |
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0162-8828 |
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LAMP; MILAB; 601.160; 600.079 |
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Admin @ si @ GaC2014 |
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2466 |
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Author |
Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu |
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Title |
Saliency for fine-grained object recognition in domains with scarce training data |
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Journal Article |
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2019 |
Publication |
Pattern Recognition |
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PR |
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94 |
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62-73 |
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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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LAMP; 600.109; 600.141; 600.120 |
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Admin @ si @ FGW2019 |
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3264 |
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