Comparison of Mid - Level Feature Coding Approaches And Pooling Strategies in Visual
نویسنده
چکیده
A number of techniques for generating mid-level features, including two variants of Soft Assignment, Locality-constrained Linear Coding, and Sparse Coding, are evaluated in the main document [1]. Pooling methods that aggregate mid-level features into vectors representing images like Average pooling, Max-pooling, and a family of likelihood inspired pooling strategies are scrutinised there. This supplementary material extends our evaluations to the PascalVOC07 dataset given Sparse Coding, as state-of-the-art classification performance it the main document is demonstrated thus far on Caltech101, Flower17, and ImageCLEF11 datasets.
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