Saliency Detection by Forward and Backward Cues in Deep-CNNs

As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not. In this paper, we propose a top-down saliency model using CNN, a weakly supervised CNN model trained for 1000 object labelling task from RGB images. The model detects attentive regions based on their objectness scores predicted by selected features from CNNs. To estimate the salient objects effectively, we combine both forward and backward features, while demonstrating that partially-guided backpropagation will provide sufficient information for selecting the features from forward run of CNN model. Finally, these top-down cues are enhanced with a state-of-the-art bottom-up model as complementing the overall saliency. As the proposed model is an effective integration of forward and backward cues through objectness without any supervision or regression to ground truth data, it gives promising results compared to state-of-the-art models in two different datasets.Comment: 5 pages,4 figures,and 1 table. the content of this work is accepted for ICIP 201

Salient object detection on hyperspectral images using features learned from unsupervised segmentation task

Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyperspectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image segmentation task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.Comment: 5 pages, 3 figures, accepted to appear in IEEE ICASSP 2019 (accepted version