Learning to rank salient segments extracted by multispectral Quantum Cuts

• A state-of-the-art multi-resolution saliency detection method is presented.
• A set of saliency maps are generated via multispectral analysis.
• Saliency maps are converted to segments, by post-processing.
• The proposed multiple segments are then ranked via a learned ranking algorithm.

In this paper, a learn-to-rank algorithm is proposed and applied over the segment pool of salient objects generated by an extension of the unsupervised Quantum-Cuts algorithm. Quantum Cuts is extended in a multiresolution approach as follows. First, superpixels are extracted from the input image using the simple linear iterative k-means algorithm; second, a scale space decomposition is applied prior to Quantum Cuts in order to capture salient details at different scales; and third, multispectral approach is followed to generate multiple proposals instead of a single proposal as in Quantum Cuts.The proposed learn-to-rank algorithm is then applied to these multiple proposals in order to select the most appropriate one. Shape and appearance features are extracted from the proposed segments and regressed with respect to a given confidence measure resulting in a ranked list of proposals. This ranking yields consistent improvements in an extensive collection of benchmark datasets containing around 18k images. Our analysis on the random forest regression models that are trained on different datasets shows that, although these datasets are of quite different characteristics, a model trained in the most complex dataset consistently provides performance improvements in all the other datasets, hence yielding robust salient object segmentation with a significant performance gap compared to the competing methods.