Learning Deep Hierarchical Visual Feature Coding

• Published in: IEEE transaction on neural networks and learning systems Vol. 25; no. 12; pp. 2212 - 2225
• Main Authors: Hanlin Goh, Thome, Nicolas, Cord, Matthieu, Joo-Hwee Lim
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Architecture; Bag-of-words (BoW) framework; Coding; Computer architecture; Computer Science; computer vision; deep learning; Dictionaries; dictionary learning; Encoding; hierarchical visual architecture; image categorization; Learning; Learning systems; Mathematical models; Neural networks; Representations; restricted Boltzmann machine (RBM); sparse feature coding; transfer learning; Visual; Visualization; deep learning; transfer learning; restricted Boltzmann machine (RBM); sparse feature coding; Bag-of-words (BoW) framework; hierarchical visual architecture; computer vision; dictionary learning; image categorization
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2014.2307532

In this paper, we propose a hybrid architecture that combines the image modeling strengths of the bag of words framework with the representational power and adaptability of learning deep architectures. Local gradient-based descriptors, such as SIFT, are encoded via a hierarchical coding scheme composed of spatial aggregating restricted Boltzmann machines (RBM). For each coding layer, we regularize the RBM by encouraging representations to fit both sparse and selective distributions. Supervised fine-tuning is used to enhance the quality of the visual representation for the categorization task. We performed a thorough experimental evaluation using three image categorization data sets. The hierarchical coding scheme achieved competitive categorization accuracies of 79.7% and 86.4% on the Caltech-101 and 15-Scenes data sets, respectively. The visual representations learned are compact and the model's inference is fast, as compared with sparse coding methods. The low-level representations of descriptors that were learned using this method result in generic features that we empirically found to be transferrable between different image data sets. Further analysis reveal the significance of supervised fine-tuning when the architecture has two layers of representations as opposed to a single layer.