Local Feature Binary Coding for Approximate Nearest Neighbor Search

IEEE Trans. Pattern Anal. Mach. Intell.

2016

Self Cite

101

In this paper, we propose a novel binary local representation for RGB-D video data fusion with a structure-preserving projection. Our contribution consists of two aspects. Toacquire a general feature for the video data, we convert the problem to describing the gradient fields of RGB and depth information of video sequences. With the local fluxes of the gradient fields, which include the orientation and the magnitude of the neighborhood of each point, a new kind of continuous local descriptor called Local Flux Feature(LFF) is obtained. Then the LFFs from RGB and depth channels are fused into a Hamming space via the Structure Preserving Projection (SPP). Specifically, an orthogonal projection matrix is applied to preserve the pairwise structure with a shape constraint to avoid the collapse of data structure in the projected space. Furthermore, a bipartite graph structure of data is taken into consideration, which is regarded as a higher level connection between samples and classes than the pairwise structure of local features. Theextensive experiments show not only the high efficiency of binary codes and the effectiveness of combining LFFs from RGB-D channels via SPP on various action recognition benchmarks of RGB-D data, but also the potential power of LFF for general action recognition.

Section: Related Workmentioning

confidence: 99%

Structure-Preserving Binary Representations for RGB-D Action Recognition

Liu

IEEE Trans. Pattern Anal. Mach. Intell.

2016

Self Cite

101

“…where x c ij is the nearest neighbor of x ij in class c and • is the L 2 norm. However, in our scheme, to reduce the complexity of NN-search in the computation of I2C distances, we first employ the K-means clustering algorithm on the set of local feature descriptors of each class [30], [31]…”

Section: Image-to-class Distancementioning

confidence: 99%

Local Feature Discriminant Projection

IEEE Trans. Pattern Anal. Mach. Intell.

Zhen

et al. 2016

Self Cite

In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. We make three novel contributions. First, the proposed LFDP is a general supervised subspace learning algorithm which provides an efficient way for dimensionality reduction of large-scale local feature descriptors. Second, we introduce the Differential Scatter Discriminant Criterion (DSDC) to the subspace learning of local feature descriptors which avoids the matrix singularity problem. Third, we propose a generalized orthogonalization method to impose on projections, leading to a more compact and less redundant subspace. Extensive experimental validation on three benchmark datasets including UIUC-Sports, Scene-15 and MIT Indoor demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification.

“…Besides, these methods are not fully linear, which limits their efficiency and applicability for large-scale data sets. In fact, one of the most related work using bilinear projection on local feature hashing can be found in [42], which is regarded as a supervised learning method for image similarity search.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Local Feature Hashing for Image Similarity Search

Liu

2016

IEEE Trans. Cybern.

Self Cite

The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global feature representations, which are susceptible to image variations such as viewpoint changes and background cluttering. Traditional global representations gather local features directly to output a single vector without the analysis of the intrinsic geometric property of local features. In this paper, we propose a novel unsupervised hashing method called unsupervised bilinear local hashing (UBLH) for projecting local feature descriptors from a high-dimensional feature space to a lower-dimensional Hamming space via compact bilinear projections rather than a single large projection matrix. UBLH takes the matrix expression of local features as input and preserves the feature-to-feature and image-to-image structures of local features simultaneously. Experimental results on challenging data sets including Caltech-256, SUN397, and Flickr 1M demonstrate the superiority of UBLH compared with state-of-the-art hashing methods.