Paper Info
Title
Sparse code LBP and SIFT features together for scene categorization
Abstract
Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT), are widely used in various computer applications. They emphasize different aspects of image contents. In this paper, we propose to sparse code them together for categorizing scene images. We first regularly extract LBP and SIFT features from the images. Then, corresponding to each feature, a visual word codebook is constructed by using training images. For creating a representation for an image, the LBP and SIFT features extracted from the same position of the image are encoded together based on sparse coding. Specifically, we combine the obtained LBP codebook and SIFT codebook as a two dimensional table. In this table, each entry corresponds to a LBP visual word and a SIFT visual word. Therefore, the encoding values of the entries depend on both features. After all features of the input image are processed, the spatial max pooling is adopted to determine the image representation. Obtained image representations are classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets scene categories 8 and MIT 67 indoor scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner has improved the scene categorization performance significantly. In addition, the results of the proposed method are comparable to other state of the art works.
Year
DOI
Venue
2014
10.1109/ICALIP.2014.7009786
Audio, Language and Image Processing
Keywords
DocType
Citations 
image coding,image representation,support vector machines,transforms,lbp codebook,mit 67 indoor scene,sift codebook,svm classifiers,feature extraction,image contents,local binary pattern,scale invariant feature transform,scene categorization,sparse code lbp,sparse code sift,spatial max pooling,visual word codebook,feature combination,lbp,sift,sparse code,computer vision,visualization,vectors,encoding
Conference
0
PageRank 
References 
Authors
0.34
30
1
Name
Order
Citations
PageRank
Shuang Bai1458.01