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Object recognition is one of the hottest research areas, which aims to recognize the objects in digital media, which can be photographs or videos. In order to recognize the objects, the objects should be detected first. The two main considerations about the object recognition system are the accuracy and time consumption rates. Taking this into account, this article presents an effective time conserving object recognition approach based on three important phases. Initially, the points of interest are selected by means of Generalized Kadir Brady (GKB) detector, which considers the geometry and texture pattern of the images. The window size is selected for extracting the contourlet and Gabor Local Vector Pattern (GLVP) features from the window. The feature vector is formed and the Extreme Learning Machine (ELM) classifier is trained, such that the ELM can recognize the objects by means of the knowledge gained in the training process. The performance of the proposed approach is evaluated in four different aspects for proving the efficacy in terms of accuracy, precision, recall, F-measure and time consumption.