Combining Descriptors Extracted from Feature Maps of Deconvolutional Networks and SIFT Descriptors in Scene Image Classification

This paper presents a new method to combine descriptors extracted from feature maps of Deconvolutional Networks and SIFT descriptors by converting them into histograms of local patterns, so the concatenation operation can be applied and ensure to increase the classification rate. We use K-means clustering algorithm to construct codebooks and compute Spatial Histograms to represent the distribution of local patterns in an image. Consequently, we can concatenate these histograms to make a new one that represents more local patterns than the originals. In the classification step, SVM associated with Histogram Intersection Kernel is utilized. In the experiments on Scene-15 Dataset containing 15 categories, the classification rates of our method are around 84% which outperforms Reconfigurable Bag-of-Words (RBoW), Sparse Covariance Patterns (SCP), Spatial Pyramid Matching (SPM), Spatial Pyramid Matching using Sparse Coding (ScSPM) and Visual Word Reweighting (VWR).