Classifier based approaches for top down salient object detection

Saliency estimation aims to identify visually important regions in an image and to inhibit distractors. It has been used in recent object detectors and image classifiers as a pre-processor to indicate possible object regions in an image. The category-independent object proposals produced by bottom-up saliency approaches include those are irrelevant for tasks like object detection. The precision of the object proposals can be improved through top-down saliency approaches that produce category-specific saliency maps. Although, the prior knowledge about object categories learnt by classifiers are useful for top-down saliency estimation, the relationship between image classifiers and top-down salient object detectors has not been explored substantially. In this thesis we develop classifier-based approaches for top-down salient object detection in which first two are trained in a fully supervised setting and the last two are trained in a weakly supervised setting. Non-linear feature representations such as sparse coding (SC) or locality constrained linear coding (LLC) cascaded with linear classifiers are proven to be effective in image classification. They are also used for top-down salient object detection to achieve a compact and discriminative representation of SIFT features, which helps to model feature selectivity for saliency map. We analyze the influence of these feature coding approaches in top-down salient object detection and also propose a novel coding strategy for top-down saliency estimation. The proposed coding strategy ensures that similar codes are assigned to the features which are adjacent in spatial, feature and category …