基于Pivots选择的有效图像块描述子

Efficient Patch-Level Descriptor for Image Categorization via Patch Pivots Selection

设计图像块特征表示是计算机视觉领域内的基本研究内容,优秀的图像块特征表示能够有效地提高图像分类、对象识别等相关算法的性能.SIFT(scale-invariant feature transform)和HOG(histogram of oriented gradient)是人为设计图像块特征表示的优秀代表,然而,人为设计图像块特征间的差异往往不能足够理想地反映图像块间的相似性.核描述子(kernel descriptor,简称KD)方法提供了一种新的方式生成图像块特征,在图像块间匹配核函数基础上,应用核主成分分析(kernel principal component analysis,简称KPCA)方法进行特征表示,且在图像分类应用上获得不错的性能.但是,该方法需要利用所有联合基向量去生成核描述子特征,导致算法时间复杂度较高.为了解决这个问题,提出了一种算法生成图像块特征表示,称为有效图像块描述子(efficient patch-level descriptor,简称EPLd).算法建立在不完整Cholesky分解基础上,自动选择少量的标志性图像块以提高算法效率,且利用MMD(maximum mean discrepancy)距离计算图像间的相似性.实验结果表明,该算法在图像/场景分类应用中获得了优秀的性能.

Designing patch-level features is essential for achieving good performance in computer vision tasks, such as image classification and object recognition. SIFT （scale-invariant feature transform） and HOG （histogram of oriented gradient） are the typical schemes among many pre-defined patch-level descriptors, but the difference between artificial patch-level features is not good enough for reflecting the similarities of images. Kernel descriptor （KD） method offers a new way to generate features from match kernel defined over image patch pairs using KPCA （kernel principal component analysis） and yields impressive results. However, all joint basis vectors are involved in the kernel descriptor computation, which is both expensive and not necessary. To address this problem, this paper presents an efficient patch-level descriptor （EPLd） which is built upon incomplete Cholesky decomposition. EPLd automatically selects a small number of image patches pivots to achieve better computational efficiency. Based on EPLd, MMD （maximum mean discrepancy） distance is used for computing similarities between images. In experiments, the EPLd approach achieves competitive results on several image/scene classification datasets compared with state-of-the-art methods.