Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
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Sparse coding based classifier (SCC) proves to lead to the state-of-the-art result in pattern recognition. Compared with traditional generative models and discriminative models, it neither casts some assumption on the distribution of data, nor learns a hyperplane to separate samples. However, SCC is characteristic of slow prediction because an l"0-norm minimization need to be solved to assign the label for each sample. In this paper, we propose a Superpixel-wise Structural Sparse Coding based Classifier (S3CC) for image segmentation. An unsupervised superpixel segmentation is first used to derive the initial labeled samples, and SCC is extended to the semi-supervised pattern where unlabeled samples are incrementally labeled and taken as the dictionary to improve the classification accuracy. Moreover, a neighborhood spatial constraint is cast on the prediction of pixel labels, to avoid the speckle-like mis-segmentation of images. Some experiments are taken on some artificial texture images, to investigate the segmentation result of our proposed S3CC. Some aspects including (1) Comparison of S3CC with SCC, (2) Comparisons of S3CC with and without spatial constraint, (3) Comparison of S3CC with semi-supervised S3CC, are tested, and the results prove the efficiency and superiority of S3CC to its counterparts.