text: We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g., young, short hair, wearing suits) and actions (e.g., running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e., a mixture of) ‘average’ templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e., it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
  @cite_24: 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 lp-norm Multiple Kernel Learning (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 object 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.
  @cite_90: This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these Exemplar-SVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such Exemplar-SVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent part-based model of , at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding.
summary: In the recently proposed Exemplar SVM (ESVM) work, Malisiewicz al @cite_90 propose to learn discriminative templates for each object instance of the training set independently and then combine their calibrated outputs on test images as a post-processing step. In contrast, we work at a part level and use all templates together during both training and testing. More recently, Yan al @cite_24 proposed a 2-level approach for image representation. Similar to our approach it involves sampling image regions, but while they vector quantize the region descriptors, we propose a mechanism to select discriminative regions and build discriminative part based models from them.