SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS

International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance

Automatic foreground extraction via joint CRF and online learning

International audienceA novel approach is proposed for automatic foreground extraction which aims to segment out all foreground objects from the background in the image. The segmentation problem is formulated as an iterative energy minimisation of the conditional random field (CRF), which can be efficiently optimised by graph-cuts. The energy minimisation is initialised and modulated by a soft location map predicted by a discriminative classifier which is learned on-the-fly from a set of segmented exemplar images. Iteratively minimising the CRF energy leads to optimal segmentation. Experimental results on the Pascal visual object classes (VOC) 2010 segmentation dataset, a widely acknowledged difficult dataset, show that the proposed approach outperforms the state-of-the-art techniques

SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS

International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance

Semantic Image Segmentation Using Region Bank

International audienceSemantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the banks. Discriminative classifiers are learned based the histograms of features descriptors computed from training region bank (TRB). Optimally merging predicted regions of query region bank (QRB) results in semantic labeling. This paper details each algorithmic module used in our system, however, any algorithm fits corresponding modules can be plugged into the proposed framework. Experiments on the challenging Microsoft Research Cambridge (MSRC 21) dataset show that the proposed approach achieves the state-of-the-art performance

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

International audienceA new approach for image retrieval by combination of colour and texture features is proposed. This approach uses the histogram of feature vectors, which are constructed from the coefficients of some subbands of wavelet transform and chosen according to their intrinsic characters. A K-means algorithm is used to quantise feature vectors. The experimental results both on small size databases (40 classes of textures) and large size databases (167 classes of textures) show that, compared with the state-of-the-art approaches, the proposed approach can achieve better retrieval performance

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

International audienceA new approach for image retrieval by combination of colour and texture features is proposed. This approach uses the histogram of feature vectors, which are constructed from the coefficients of some subbands of wavelet transform and chosen according to their intrinsic characters. A K-means algorithm is used to quantise feature vectors. The experimental results both on small size databases (40 classes of textures) and large size databases (167 classes of textures) show that, compared with the state-of-the-art approaches, the proposed approach can achieve better retrieval performance