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This work investigates how the traditional image classification pipelines canbe extended into a deep architecture, inspired by recent successes of deepneural networks. We propose a deep boosting framework based on layer-by-layerjoint feature boosting and dictionary learning. In each layer, we construct adictionary of filters by combining the filters from the lower layer, anditeratively optimize the image representation with a jointdiscriminative-generative formulation, i.e. minimization of empiricalclassification error plus regularization of analysis image generation overtraining images. For optimization, we perform two iterating steps: i) tominimize the classification error, select the most discriminative featuresusing the gentle adaboost algorithm; ii) according to the feature selection,update the filters to minimize the regularization on analysis imagerepresentation using the gradient descent method. Once the optimization isconverged, we learn the higher layer representation in the same way. Our modeldelivers several distinct advantages. First, our layer-wise optimizationprovides the potential to build very deep architectures. Second, the generatedimage representation is compact and meaningful. In several visual recognitiontasks, our framework outperforms existing state-of-the-art approaches.

Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy