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

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

Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation

Despite the tremendous achievements of deep convolutional neural networks (CNNs) in many computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein. Our proposed method starts out with a layer-wise relevance propagation (LRP) step which estimates a pixel-wise relevance map over the input image. Following, we construct a context-aware saliency map, SR map, from the LRP-generated map which predicts areas close to the foci of attention instead of isolated pixels that LRP reveals. In human visual system, information of regions is more important than of pixels in recognition. Consequently, our proposed approach closely simulates human recognition. Experimental results using the ILSVRC2012 validation dataset in conjunction with two well-established deep CNN models, AlexNet and VGG-16, clearly demonstrate that our proposed approach concisely identifies not only key pixels but also attention areas that contribute to the underlying neural network's comprehension of the given images. As such, our proposed SR map constitutes a convenient visual interface which unveils the visual attention of the network and reveals which type of objects the model has learned to recognize after training. The source code is available at https://github.com/Hey1Li/Salient-Relevance-Propagation.Comment: 35 pages, 15 figure