Deep Learning based Salient Object Detection

Guanbin Li Yizhou Yu

The University of Hong Kong

List of Papers

1. Guanbin Li and Yizhou Yu, Deep Contrast Learning for Salient Object Detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016. [BibTex] (paper)

2. Guanbin Li and Yizhou Yu, Visual Saliency Detection Based on Multiscale Deep CNN Features, IEEE Transactions on Image Processing (TIP), Vol 25, No 11, pp.5012-5024, 2016. (paper)

3. Guanbin Li and Yizhou Yu, Visual Saliency Based on Multiscale Deep Features, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015. [BibTex] (paper | supplemental)

Our HKU-IS Dataset

The HKU-IS dataset can be downloaded from Google Drive or Baidu Yun. Please cite our paper if you use this dataset.

Deep Contrast Learning for Salient Object Detection

1. Abstract

Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.

2. Architecture

Two streams of our deep contrast network

3. Results

Visual comparison of saliency maps generated from state-of-the-art methods, including our DCL and DCL⁺. The ground truth (GT) is shown in the last column. DCL⁺ consistently produces saliency maps closest to the ground truth.

4. Quantitative Comparison

Comparison of precision-recall curves of 11 saliency detection methods on 3 datasets. Our MDF, DCL and DCL⁺ (DCL with CRF) consistently outperform other methods across all the testing datasets.

Comparison of precision, recall and F-measure (computed using a per-image adaptive threshold) among 11 different methods on 3 datasets.

Comparison of quantitative results including maximum F-measure (larger is better) and MAE (smaller is better). The best three results are shown in red, blue, and green , respectively.

5. Downloads

(1) The trained model and the test code for DCL saliency can be downloaded from google drive or Baidu Yun.

(2) Saliency maps of our approach on 6 benchmark data sets can be download here.

Visual Saliency Detection Based on Multiscale Deep CNN Features

1. Abstract

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10.0% respectively on the DUT-OMRON dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 9% and 35.3% respectively on these two datasets.

2. Architecture

The architecture of Multiscale Deep CNN Features

3. Results

Visual comparison of saliency maps generated from 10 state-of-the-art methods, including our two models MDF and HDHF. The ground truth (GT) is shown in the last column. MDF and HDHF consistently produce saliency maps closest to the ground truth.

4. Quantitative Comparison

Comparison of precision-recall curves of 15 saliency detection methods on 3 datasets. Our MDF and HDHF based models consistently outperform other methods across all the testing datasets.

Comparison of precision, recall and F-measure (computed using a per-image adaptive threshold) among 15 different methods on 3 datasets.

Comparison of quantitative results including AUC (larger is better), maximum F-measure (larger is better) and MAE (smaller is better). The best three results are shown in red , blue , and green color, respectively.

5. Downloads

(1) The trained model and test code for HDHF saliency (TIP2016) will be released soon.

(2) To download the trained model and test code for MDF saliency (cvpr 2015), please click here. 

(3) Saliency maps of our approach on 9 benchmark data sets can be download here. Including MSRA_B(test part), HKU-IS(test part), ICOSEG,  SED1, SED2, SOD, Pascal-s, Dut-Omron, ECSSD.