A Fast Sparse Coding Method for Image Classification

Abstract

Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.

Method of raw materials selection for production of the MgO‐C bricks of comparable properties using PCA and K‐medoids

“…Partitioning clustering is useful for classifying observations within a data set into multiple groups based on the observations similarity. This method is commonly used in scientific research 45–50 . The main advantage of K‐medoids algorithm is that it is more robust to data anomalies such outliers or missing values than commonly used K‐means algorithm 51 .…”

“…This method is commonly used in scientific research. [45][46][47][48][49][50] The main advantage of K-medoids algorithm is that it is more robust to data anomalies such outliers or missing values than commonly used K-means algorithm. 51 The term medoid refers to an object within a cluster for which the average dissimilarity measure between the object and other objects in the cluster is minimal.…”

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

“…Deep neural networks [8] have been widely used to learn low-dimensional feature representations to reduce the dimensionality of VHR images. Sparse coding [43] is another famous unsupervised feature learning method, which is highly effective for scene classification when compared to the traditional Bag of Visual Words (BoVW)-based approaches [44] and generates a set of basic functions from the unlabeled data. Recently, a method combining scale-invariant-feature-transform (SIFT)-based feature descriptors and sparse coding (Sift + SC) has been put forward.…”