Integrating absolute distances in collaborative representation for robust image classification

Conventional sparse representation based classification （SRC） represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification （CRC） obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals rescrc with absolute distance vector disabs and generate a new dviaetion r = a·rescrc b.disabs, which is in turn used to perform classification. Because the two residuals have opposite effect in classification, the method uses a subtraction operation to perform fusion. We conducted extensive experiments to evaluate our method for image classification with different instantiations. The experimental results indicated that it produced a more promising result of classification on both facial and non-facial images than original CRC method.

Face Recognition Algorithm Fusing Monogenic Binary Coding and Collaborative Representation

Monogenic binary coding （MBC） have been known to be effective for local feature extraction, while sparse or collaborative representation based classification （CRC） has shown interesting results in robust face recognition. In this paper, a novel face recognition algorithm of fusing MBC and CRC named M-CRC is proposed; in which the dimensionality problem is resolved by projection matrix. The proposed algorithm is evaluated on benchmark face databases, including AR, PolyU-NIR and CAS-PEAL. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods.

Weighted average integration of sparse representation and collaborative representation for robust face recognition

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification.As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition.The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10%improvement in recognition accuracy.

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image(HSI)contains a wealth of spectral information,which makes fine classification of ground objects possible.In the meanwhile,overly redundant information in HSI brings many challenges.Specifically,the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier.In order to solve these problems,dimensionality reduction is usually adopted.Recently,graph-based dimensionality reduction has become a hot topic.In this paper,the graph-based methods for HSI dimensionality reduction are summarized from the following aspects.1)The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space.2)The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary.3)Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information,local intra-class information and spatial information.In order to compare typical techniques,three real HSI datasets were used to carry out relevant experiments,and then the experimental results were analysed and discussed.Finally,the future development of this research field is prospected.

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background:As the demand for traditional Chinese medicinal materials increases in China and even the world,there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication.Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification.Both of these techniques require the use of stoichiometric methods in the identification process.Although they have high accuracy and sensitivity,they are expensive and inefficient.In addition,near-infrared spectroscopy is a fast,nondestructive,and widely used identification technique developed in recent years,but its identification results are susceptible to samples’states and environmental conditions,and its sensitivity is low.Hyperspectral imaging combines the advantages of imaging technology and optical technology,which can simultaneously access the image information and spectral information which reflect the external characteristics,internal physical structure,and chemical composition of the samples.Hyperspectral imaging is widely applied to agricultural product inspection,but research into its application in origin and quality identification of TCM materials is rare.Methods:In this study,the algorithm framework discriminative marginalized least squares regression(DMLSR)was used for feature extraction of frankincense hyperspectral data.The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples.Then,the discriminative collaborative representation with Tikhonov regularization(DCRT)was applied for classifying the geographical origin and level of frankincense.DCRT introduces the discriminant regularization term and incorporates SID,which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM.Results:For the origin classification task,samples of all levels from each origin were,respectively,selected for three-way classification.We used 10-fold cross-validation to select a model parameter in the experiment.When obtaining the optimal parameters,we randomly selected the training set and testing set,where the training set accounts for 70%and the training set for 30%.After repeating this random process 10 times,we obtained the final average classification accuracy,which is higher than 90%,and the standard deviation fluctuation is usually small.For the level classification task,samples of each level from three origins were separately selected for multiclassification.We randomly selected the training set and testing set from each origin.The level classification results of the three origins are good on D4350 data,and the classification accuracy of each level is basically above 80%.Conclusion:Experiments and analysis show that our algorithm framework has excellent classification performance,which is stable in origin classification and has potential for generalization.In addition,the experiments show that in our algorithm framework,different classification tasks need to combine different data sources to achieve better classification and recognition,as the origin classification task uses frankincense’s D3000 data,and level classification task uses frankincense’s D4350 data.