Snapshot multispectral imaging suffers from severely low spatial resolution and degraded signals due to mosaic rearrangement. In order to recover a signal of full bands and full sensor size from a single snapshot, we propose a self-dictionary-transformed tensor nuclear norm and develop a joint optimization with total variation regularization as a convex completion problem. The proposed nuclear norm is designed specifically for the intrinsic structure of snapshot multispectral data, reflects the parsimony of tensors that conventional approaches ignore, as well as incorporates inter-axis correlation unlike matrix-based optimization. We show increased accuracy with our self-dictionary throughout simulation experiments and demonstrate quality enhancement in recovering real snapshot multispectral images.

In this paper, we propose a learning-based supervised discrete hashing method. Binary hashing is widely used for large-scale image retrieval as well as video and document searches because the compact binary code representation is essential for data storage and reasonable for query searches using bit-operations. The recently proposed supervised discrete hashing (SDH) method efficiently solves mixed-integer programming problems by alternating optimization and the discrete cyclic coordinate descent (DCC) method. Based on some preliminary experiments, we show that the SDH method can be simplified without performance degradation. We analyze the simplified model and provide a mathematically exact solution thereof; we reveal that the exact binary code is provided by a "Hadamard matrix." Therefore, we named our method Hadamard codedsupervised discrete hashing (HC-SDH). In contrast to SDH, our model does not require an alternating optimization algorithm and does not depend on initial values. HC-SDH is also easier to implement than iterative quantization (ITQ). Experimental results involving a large-scale database show that Hadamard coding outperforms conventional SDH in terms of precision, recall, and computational time. On the large datasets SUN-397 and ImageNet, HC-SDH provides a superior mean average of precision (mAP) and top-accuracy compared to the conventional SDH methods with the same code length and FastHash. The training time of HC-SDH is 170 times faster than conventional SDH and the testing time including the encoding time is seven times faster than FastHash which encodes using a binary-tree.

We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter. Although conventional affine region detectors, such as Hessian/Harris-Affine, iterate to find an affine region that fits a given image patch, such iterative searching is adversely affected by an initial point. To avoid this problem, we allow multiple detections from a single keypoint. We demonstrate that the responses of all possible anisotropic LoG filters can be efficiently computed by factorizing them in a similar manner to spectral SIFT. A large number of LoG filters that are densely sampled in a parameter space are reconstructed by a weighted combination of a limited number of representative filters, called "eigenfilters", by using singular value decomposition. Also, the reconstructed filter responses of the sampled parameters can be interpolated to a continuous representation by using a series of proper functions. This results in efficient multiple extrema searching in a continuous space. Experiments revealed that our method has higher repeatability than the conventional methods.

When the FAST method for detecting corner features at high speed is applied to images that include complex textures (regions that include foliage, shrubbery, etc.), many corners that are not needed for object recognition are detected because FAST defines corner features on the basis of a 16-pixel bounding circle. To overcome that problem, we propose the Cascaded FAST that defines corners on the basis of similarity in terms of intensity, continuity and orientation in a broader range of areas (20, 16, and 12 pixel bounding circles). Also, cascading three decision trees trained by the FAST approach enables high-speed corner detection in which non-corners are eliminated early in the process. Furthermore, Cascaded FAST determines scale by using an image pyramid and determines orientation at high speed by using a framework for referencing surrounding pixels.

We propose Compact And Real-time Descriptors (CARD) which can be computed very rapidly and be expressed by short binary codes. An efficient algorithm based on lookup tables is presented for extracting histograms of oriented gradients, which results in approximately 16 times faster computation time per descriptor than that of SIFT. Our lookup-table-based approach can handle arbitrary layouts of bins, such as the grid binning of SIFT and the log-polar binning of GLOH, thus yielding sufficient discrimination power. In addition, we introduce learning-based sparse hashing to convert the extracted descriptors to short binary codes. This conversion is achieved very rapidly by multiplying a very sparse integer weight matrix by the descriptors and aggregating signs of their multiplications. The weight matrix is optimized in a training phase so as to make Hamming distances between encoded training pairs reflect visual dissimilarities between them. Experimental results demonstrate that CARD outperforms previous methods in terms of both computation time and memory usage.

This paper proposes Multiclass VisualRank, a method that expands the idea of VisualRank into more than one category of images. Multiclass VisualRank divides images retrieved from search engines into several categories based on distinctive patterns of visual features, and gives ranking within the category. Experimental results show that our method can extract several different image categories relevant to given keyword and gives good ranking scores to retrieved images.