Saliency Map improvement using edge-aware filtering

by Diptiben Patel and Shanmuganathan Rama

Human skin segmentation improved by saliency detection

Several applications demand the segmentation of images in skin and non-skin regions, such as face recognition, hand gesture detection, nudity recognition, among others. Human skin detection is still a challenging task since it depends on inumerous factors, for instance, illumination conditions, ethnicity variation and image resolution. This work proposes and analyzes a skin segmentation method improved by saliency detection. Experimental results on public data sets demonstrate significant improvement of the proposed skin segmentation method over state-of-the-art approaches.Several applications demand the segmentation of images in skin and non-skin regions, such as face recognition, hand gesture detection, nudity recognition, among others. Human skin detection is still a challenging task since it depends on inumerous factors9257146157FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORsem informaçãosem informaçãosem informação16th International Conference, CAIP 201

Learning feature fusion strategies for various image types to detect salient objects

Salient object detection is the task of automatically localizing objects of interests in a scene by suppressing the background information, which facilitates various machine vision applications such as object segmentation, recognition and tracking. Combining features from different feature-modalities has been demonstrated to enhance the performance of saliency prediction algorithms and different feature combinations are often suited to different types of images. However, existing saliency learning techniques attempt to apply a single feature combination across all image types and thus lose generalization in the test phase when considering unseen images. Learning classifier systems (LCSs) are an evolutionary machine learning technique that evolve a set of rules, based on a niched genetic reproduction, which collectively solve the problem. It is hypothesized that the LCS technique has the ability to autonomously learn different feature combinations for different image types. Hence, this paper further investigates the application of LCS for learning image dependent feature fusion strategies for the task of salient object detection. The obtained results show that the proposed method outperforms, through evolving generalized rules to compute saliency maps, the individual feature based methods and seven combinatorial techniques in detecting salient objects from three well known benchmark datasets of various types and difficulty levels.</p

Multi-level net: A visual saliency prediction model

State of the art approaches for saliency prediction are based on Fully Convolutional Networks, in which saliency maps are built using the last layer. In contrast, we here present a novel model that predicts saliency maps exploiting a non-linear combination of features coming from different layers of the network. We also present a new loss function to deal with the imbalance issue on saliency masks. Extensive results on three public datasets demonstrate the robustness of our solution. Our model outperforms the state of the art on SALICON, which is the largest and unconstrained dataset available, and obtains competitive results on MIT300 and CAT2000 benchmarks.State of the art approaches for saliency prediction are based on Full Convolutional Networks, in which saliency maps are built using the last layer. In contrast, we here present a novel model that predicts saliency maps exploiting a non-linear combination of features coming from different layers of the network. We also present a new loss function to deal with the imbalance issue on saliency masks. Extensive results on three public datasets demonstrate the robustness of our solution. Our model outperforms the state of the art on SALICON, which is the largest and unconstrained dataset available, and obtains competitive results on MIT300 and CAT2000 benchmarks