An Image Classification Algorithm Based on Bag of Visual Words and Multi-kernel Learning

Abstract

In this article, we propose an image classification algorithm based on Bag of Visual Words model and multikernel learning. First of all, we extract the D-SIFT (Dense Scale-invariant Feature Transform) features from images in the training set. And then construct visual vocabulary via K-means clustering. The local features of original images are mapped to vectors of fixed length through visual vocabulary and spatial pyramid model. At last, the final classification results are given by generalized multiple kernel proposed by this paper. The experiments are performed on Caltech-101 image dataset and the results show the accuracy and effectiveness of the algorithm.

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

“…Salient point descriptors that are invariant to scale and orientation are most appropriate to build an image classification model that is robust to scale and rotation. Numerous such salient point detection methods are available, with SIFT and SURF commonly being used in the BoW context [19]. In this study, SURF was used since it is faster than SIFT and its descriptor is suitable to be used as the feature in the BoW framework, as discussed in the following sub-section.…”

“…However, most global features are very sensitive to scale and clutter [17]. In contrast, the local descriptors are robust to clutter but cannot capture the global characteristics of the image [18,19]. An alternate feature representation strategy, such as Visual-Bag-of-Words (BoW), captures the global characteristics of the image through encoding a set of local features, which makes them robust to scale and clutter [20,21].…”

Classification of Test Documents Based on Handwritten Student ID's Characteristics

“…The final results show that the classification method based on one hidden layer is effective in image classification and performs better than present algorithms of the same kind. [15] …”

“…The feature extraction step is the same in testing process, and the same vocabulary is used in BoW feature summarization. [15] …”

“…Using Bag of Words model in image classification not only solves the problem brought by the disunity of local features, but also brings the advantages of easy expression. Now the method is extensively used in image classification and retrieval [15].…”