Salient object detection by composition

Conventional saliency analysis methods measure the saliency of individual pixels. The resulting saliency map inevitably loses information in the original image and finding salient objects in it is difficult. We propose to detect salient objects by directly measuring the saliency of an image window in the original image and adopt the well established sliding window based object detection paradigm. We present a simple definition for window saliency, i.e., the cost of composing the window using the remaining parts of the image. The definition uses the entire image as the context and agrees with human intuition. It no longer relies on idealistic assumptions usually used before (e.g., "back- ground is homogenous") and generalizes well to complex objects and backgrounds in real world images. To realize the definition, we illustrate how to incorporate different cues such as appearance, position, and size. Based on a segment-based representation, the window composition cost function can be efficiently evaluated by a greedy optimization algorithm. Extensive evaluation on challenging object detection datasets verifies better efficacy and efficiency of the proposed method comparing to the state-of-the-art, making it a good pre-processing tool for subsequent applications. Moreover, we hope to stimulate further work towards the challenging yet important problem of generic salient object detection.