Object recognition is a complex and challenging problem. It involves examining many different hypothesis in terms of the object class, position, scale, pose, etc., but the main trend in computer vision systems is to lazily rely on the brute force capacity of computers, that is to explore every possibilities indifferently. Sadly, in many case this scheme is way too slow for real-time or even practical applications. By incorporating salience in the recognition process, several approaches have shown that it is possible to get several orders of speed-up. In this chapter, we demonstrate the link between salience and cascaded processes and show why and how those ones should be constructed. We illustrate the benefits that it provides, in terms of detection speed, accuracy and robustness, and how it eases the combination of heterogeneous feature types (i.e. dense and sparse features) by some innovating strategies from the state-of-the-art and a practical application.