Neural Computation
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Generic Object Recognition with Boosting
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Object Detection with Discriminatively Trained Part-Based Models
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Modeling the temporal extent of actions
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Improving the fisher kernel for large-scale image classification
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Image classification using super-vector coding of local image descriptors
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International Journal of Computer Vision
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In this paper we propose a generic framework to incorporate unobserved auxiliary information for classifying objects and actions. This framework allows us to automatically select a bounding box and its quadrants from which best to extract features. These spatial subdivisions are learnt as latent variables. The paper is an extended version of our earlier work Bilen et al. (Proceedings of The British Machine Vision Conference, 2011), complemented with additional ideas, experiments and analysis. We approach the classification problem in a discriminative setting, as learning a max-margin classifier that infers the class label along with the latent variables. Through this paper we make the following contributions: (a) we provide a method for incorporating latent variables into object and action classification; (b) these variables determine the relative focus on foreground versus background information that is taken account of; (c) we design an objective function to more effectively learn in unbalanced data sets; (d) we learn a better classifier by iterative expansion of the latent parameter space. We demonstrate the performance of our approach through experimental evaluation on a number of standard object and action recognition data sets.