IEEE Trans Image Process - Multilinear supervised neighborhood embedding of a local descriptor tensor for scene/object recognition.

Tópicos

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Resumo

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).

Resumo Limpo

paper propos repres imag local descriptor tensor use multilinear supervis neighborhood embed msne discrimin featur extract abl use subject scene recognit contribut paper includ novel featur extract approach denot histogram orient weight normal gradient nhog local region represent robust larg illumin variat imag imag represent framework denot local descriptor tensor can effect combin moder amount local featur togeth imag represent effici popular exist bagoffeatur model msne analysi algorithm can direct deal local descriptor tensor extract discrimin compact featur time preserv neighborhood structur tensorfeatur space subjectscen recognit demonstr perform advantag propos approach exist techniqu differ type benchmark databas scene data set ie ot face data set ie yale pie viewbas object data set coil eth

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