Many state-of-the-art methods in object recognition extract features from an image and encode them, followed by a pooling step and classification. Within this processing pipeline, often the encoding step is the bottleneck, for both computational efficiency and performance. We present a novel assignment-based encoding formulation. It allows for the fusion of assignment-based encoding and sparse coding into one formulation. We also use this to design a new, very efficient, encoding. At the heart of our formulation lies a quantization into a set of k-sparse vectors, which we denote as sparse quantization. We design the new encoding as two nested, sparse quantizations. Its efficiency stems from leveraging bit-wise representations. In a series of experiments on standard recognition benchmarks, namely Caltech 101, PASCAL VOC 07 and ImageNet, we demonstrate that our method achieves results that are competitive with the state-of-the-art, and requires orders of magnitude less time and memory. Our method is able to encode one million images using 4 CPUs in a single day, while maintaining a good performance. © 2012 Springer-Verlag.
CITATION STYLE
Boix, X., Roig, G., Leistner, C., & Van Gool, L. (2012). Nested sparse quantization for efficient feature coding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7573 LNCS, pp. 744–758). https://doi.org/10.1007/978-3-642-33709-3_53
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