Abstract

We introduce a new framework for image classification that extends beyond the window sampling of fixed spatial pyramids to include a comprehensive set of windows densely sampled over location, size and aspect ratio. To effectively deal with this large set of windows, we derive a concise high-level image feature using a two-level extraction method. At the first level, window-based features are computed from local descriptors (e.g., SIFT, spatial HOG, LBP) in a process similar to standard feature extractors. Then at the second level, the new image feature is determined from the window-based features in a manner analogous to the first level. This higher level of abstraction offers both efficient handling of dense samples and reduced sensitivity to misalignment. More importantly, our simple yet effective framework can readily accommodate a large number of existing pooling/coding methods, allowing them to extract features beyond the spatial pyramid representation. To effectively fuse the second level feature with a standard first level image feature for classification, we additionally propose a new learning algorithm, called Generalized Adaptive *p -norm Multiple Kernel Learn- ing (GA-MKL), to learn an adapted robust classifier based on multiple base kernels constructed from image features and multiple sets of pre- learned classifiers of all the classes. Extensive evaluation on the ob ject recognition (Caltech256) and scene recognition (15Scenes) benchmark datasets demonstrates that the proposed method outperforms state-of- the-art image classification algorithms under a broad range of settings.