CAJ | 학술논문

在基于稀疏表示分类的模式识别中,字典学习可以为稀疏表示获得更为精简的数据表示.然而,字典大小是衡量识别精度和速度的重要因素,优化字典设计能同时满足这两方面的需求.文中提出了一种新的技术叫作基于竞争聚集的K奇异值字典学习方法(CA-KSVD).该方法优化了字典的大小,并同时保证了识别的准确率.CA-KSVD将竞争聚集算法中优化簇数的原理引入K-SVD,从而提高了K-SVD的字典学习能力.优化过程从输入大量字典原子开始,逐步减少那些未充分利用或相似的原子,最后得到高性能的字典,它不再包含那些冗余的原子.Extend YaleB和AR人脸数据库上的实验结果表明了文中算法的有效性.
재기우희소표시분류적모식식별중,자전학습가이위희소표시획득경위정간적수거표시.연이,자전대소시형량식별정도화속도적중요인소,우화자전설계능동시만족저량방면적수구.문중제출료일충신적기술규작기우경쟁취집적K기이치자전학습방법(CA-KSVD).해방법우화료자전적대소,병동시보증료식별적준학솔.CA-KSVD장경쟁취집산법중우화족수적원리인입K-SVD,종이제고료K-SVD적자전학습능력.우화과정종수입대량자전원자개시,축보감소나사미충분이용혹상사적원자,최후득도고성능적자전,타불재포함나사용여적원자.Extend YaleB화AR인검수거고상적실험결과표명료문중산법적유효성.
In pattern recognition based on sparse representation classification,concise representation of date can be obtained for sparse rep-resentation via dictionary learning. However,the size of dictionary is an important tradeoff between recognition speed and accuracy,the design of optimized dictionary can satisfy requirements of two aspects simultaneously. A novel technique called the K -SVD dictionary learning algorithm based on competitive agglomeration (CA-KSVD) is proposed,which finds a dictionary with optimized size without compromising its recognition accuracy. CA-KSVD improves the K-SVD dictionary learning algorithm by introducing a mechanism to K-SVD,the mechanism in competitive agglomeration can optimize dictionary size. Optimization procedure starts with a large number of dictionary atoms and gradually reduces the under-utilized or similar atoms to produce a high-performance dictionary that has no redun-dant atoms. Experimental results with Extend YaleB and AR databases demonstrate the effectiveness of the method in this paper.