To compare the four salient object detection methods introduced in Section 3.3, we have performed experiments on close-range hyperspectral images from two online datasets. The first dataset was collected by Foster et al. [89, 82]. It contains in total 55 hyperspectral images of natural scenes, with 16 images available online for free access. The images consist of 33 bands, covering the spectral range from 400 to 720nm. Due to the low signal to noise ratio at two ends of the range, the first and last bands are removed for some scenes. The second dataset consists of 50 hyperspectral images collected at Harvard University [90], which includes images captured in both indoor and outdoor settings. The wavelength that this dataset covers is from 420 to
Figure 3.5: Conspicuity maps computed from different methods. From left to right: input image, conspicuity maps from intensity (I), orientation (O), trichromatic col- or (C), spectral band opponent (G), spectral Euclidean distance (E), spectral angle
distance (A), and spectral-spatial distribution (S).
720nm, containing 31 bands.
It should be noted that these two datasets were not collected specifically for saliency object detection purpose. Therefore, in most of the images, it is hard to find salient objects or the scenes are cluttered with many objects. We have careful- ly selected image regions that contain salient objects in their surroundings from 13 images in these datasets for our experiments. To provide the ground truth, we have manually labeled the location of salient objects by bounding boxes. Because spectral saliency is not directly observable to human eyes, we have combined visual salien- cy on synthesized RGB images and domain knowledge on the object materials for judgment.
We have implemented both Itti’s method and the proposed methods. The first method based on converted RGB images consists of three conspicuity maps comput- ed from intensity, orientation, and color, respectively. We denote them as “I", “O" and “C". The second method based on grouped spectral band opponent is named “G". We have implemented two versions of the third method, i.e., using Euclidean dis- tance of spectral response “E" and the spectral angle distance “A". The last method
Figure 3.6: Saliency maps computed from different combinations of conspicuity map- s. From left to right: input image, IOC, EAS, EASG, EOA, EOG, IOE and the method
in [79].
employed is the spectral-spatial distribution based method and denoted as “S". We have calculated the above conspicuity maps for the all 13 scenes, four of which are shown in Fig. 3.5. From this figure, we have a few observations. Firstly, if comparing the third column with fourth to eight columns, the conspicuity maps from spectral bands are generally better than those from trichromatic color. The method “C" com- pletely fails to highlight the objects in the third and fourth scenes. Furthermore, results from methods “E" and “A" are complementary to each other. They are able to emphasize different parts of the object in the scene. It is probably because they make use of different characteristic of distance measurements. At last, method “S" achieves excellent results. Although it mistakenly highlights the leaves on the top of the second scene, it successfully extracts the whole bottle in the fourth scene. Overall, this experiment indicates that the spectral bands based saliency detection methods perform better than the traditional one based on trichromatic color.
The saliency maps are usually generated by linearly combining several conspicu- ity maps. Here, we have tested a series of different combinations of conspicuity maps from intensity, orientation, color, group opponents, Euclidean distance, spectral an-
Figure 3.7: Salient object detection results. From left to right: manually labeled image, results computed from IOC, EAS, EASG, EOA, EOG, IOE and the method
in [79].
gle distance and spectral-spatial distribution. For the sake of briefness and clearness, we denote these methods with the abbreviation introduced for the conspicuity map. For instance, the saliency map associating the conspicuity map from the intensity, orientation, and color is denoted as “IOC". The saliency map using the group oppo- nents, Euclidean distance, spectral angle distance and spectral-spatial distribution is denoted as “EASG". Therefore, the methods tested include IOC, EAS, EASG, EOA, EOG, IOE. These methods are compared against an alternative method from Moan et al. [79]. The method from Moan et al. firstly combines spectral channels into red, green, and blue groups, then computes the spectral differences on each of the col- or groups to get the spectral saliency. The orientation saliency is extracted on the first principle component generated by principle component analysis to reduce the dimensionality of the hyperspectral image, while the intensity saliency is not used.
Fig. 3.6 and 3.7 display the saliency map and corresponding object detection re- sults generated by each method under comparison. It can be observed that spectral information based methods can generate better saliency features than that based on color information. After combining all spectral-based methods, EASG achieves the
Figure 3.8: Precision recall curves computed from different saliency detection meth- ods.
best result. When it comes to object detection, the spectral-based methods can de- tect the truth object more accurately, especially EAS and EASG. In contrast, IOA either includes plenty of background regions into the results or detects parts of the salient object. Moan’s method, although achieved good results, also includes some background regions into the final results.
To provide quantitative analysis to the saliency object detection methods, we have calculated the precision and recall curve when different binarization thresholds are used. The results are shown in Fig. 3.8. The precision is the percentage of true object pixels out of all detected pixels. The recall calculates the percentage of true object pixels that have been detected. It can be seen that the spectral saliency meth- ods are clearly better than the method based on color images. The performance of the spectral-based solutions are very close to each other, with the EASG option out- performing the others. Furthermore, we have calculated the F-Measure to evaluate different methods. F-Measure is a comprehensive used evaluating indicator to eval- uate the quality of a saliency map. It is calculated as a weighted harmonic mean
Figure 3.9: F-Measure computed from different saliency detection methods.
of precision and recall. For each scene, we can obtain an F-Measure value on the binary map of saliency map. Then the final F-Measure is computed as the average F-Measure value in the datasets. The results are shown in Fig. 3.9. It can be noted that EAS and EASG achieved significantly higher value than the rest of methods. It is consistent with our previous observations that the proposed features E,A,S and G have better prediction on the salient objects than traditional methods I, O and C. These experimental results show the advantage of combining different spectral and spatial based measures for saliency detection.