Multilinear Supervised Neighborhood Embedding of a Local Descriptor Tensor for Scene/Object Recognition

• Published in: IEEE transactions on image processing Vol. 21; no. 3; pp. 1314 - 1326
• Main Authors: HAN, Xian-Hua, CHEN, Yen-Wei, XIANG RUAN
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Feature extraction; Histograms; Image processing; Image recognition; Image representation; Information, signal and communications theory; Lighting; Local descriptor tensor; Mathematical analysis; Mathematical models; multilinear supervised embedding learning; normalized gradient; Pattern recognition; Recognition; Representations; SIFT; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Tensile stress; tensor analysis; Tensors; Performance evaluation; tensor analysis; Discriminant analysis; Histogram; SIFT; Pattern recognition; Object recognition; Signal representation; Algorithm; Supervised learning; Object detection; Local descriptor tensor; Database; Image representation; Signal processing; multilinear supervised embedding learning; Illumination; Feature extraction; Tensor calculus; normalized gradient; Target detection
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2011.2168417

In this paper, we propose to represent an image as a local descriptor tensor and use a multilinear supervised neighborhood embedding (MSNE) for discriminant feature extraction, which is able to be used for subject or scene recognition. The contributions of this paper include: (1) a novel feature extraction approach denoted as the histogram of orientation weighted with a normalized gradient (NHOG) for local region representation, which is robust to large illumination variation in an image; (2) an image representation framework denoted as the local descriptor tensor, which can effectively combine a moderate amount of local features together for image representation and be more efficient than the popular existing bag-of-feature model; and (3) an MSNE analysis algorithm, which can directly deal with the local descriptor tensor for extracting discriminant and compact features and, at the same time, preserve neighborhood structure in tensor-feature space for subject/scene recognition. We demonstrate the performance advantages of our proposed approach over existing techniques on different types of benchmark database such as a scene data set (i.e., OT8), face data sets (i.e., YALE and PIE), and view-based object data sets (COIL-100 and ETH-80).