Deep Saliency with Encoded Low level Distance Map and High Level Features

Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1X1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern Recognition(CVPR) 2016. Project page: https://github.com/gylee1103/SaliencyEL

Recurrent Attentional Networks for Saliency Detection

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.Comment: CVPR 201

Saliency detection

Postoje mnoge metode za detekciju istaknutih dijelova slike, a neke od njih su: metoda detekcije pomoću frekvencije, metoda detekcije pomoću globalnog i lokalnog kontrasta te metoda detekcije pomoću konteksta. Metoda detekcije pomoću frekvencije koristi prostornu frekvenciju. Metoda detekcije pomoću globalnog kontrasta koristi histograme ili regije. Metoda detekcije na temelju lokalnog kontrasta koristi filtre. Metoda detekcije pomoću konteksta jedina izdvaja i kontekst slike te daje dobre rezultat ukoliko postoji barem jedan istaknuti objekt na slici koji se razlikuje od svoje pozadineThere are a lot of methods for detection of salient image regions, and some of them are: frequency based saliency detection, local and global contrast based saliency detection and context-aware saliency detection. Frequency based saliency detection uses spatial frequencies. Local contrast based saliency detection uses histograms or regions. Global contrast based saliency detection uses filters. Context-aware saliency detection is the only detection that includes context and provides good results if there is at least one salient object in the picture which differs from its background