Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.Comment: 12 page

Automatic Query Image Disambiguation for Content-Based Image Retrieval

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.Comment: VISAPP 2018 paper, 8 pages, 5 figures. Source code: https://github.com/cvjena/ai

ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and Astrophysic

A golden block based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only

Searching for massive galaxies at z>=3.5 in GOODS-North

We constrain the space density and properties of massive galaxy candidates at redshifts of z>=3.5 in the GOODS-N field. By selecting sources in the Spitzer+IRAC bands, a highly stellar mass-complete sample is assembled,including massive galaxies which are very faint in the optical/near-IR bands that would be missed by samples selected at shorter wavelengths. The z>=3.5 sample was selected down to 23 mag at 4.5 micron using photometric redshifts that have been obtained by fitting the galaxies SEDs at optical, near-IR and IRAC bands. We also require that the brightest band in which candidates are detected is the IRAC 8 micron band in order to ensure that the near-IR 1.6 micron (rest-frame) peak is falling in or beyond this band. We found 53 z>=3.5 candidates, with masses in the range of M~10^10-10^11M_sun. At least ~81% of these galaxies are missed by traditional Lyman Break selection methods based on UV light. Spitzer+MIPS emission is detected for 60% of the sample of z>=3.5 galaxy candidates. Although in some cases this might suggest a residual contamination from lower redshift star-forming galaxies or AGN, 37% of these objects are also detected in the sub-mm/mm bands in recent SCUBA,AzTEC and MAMBO surveys, and have properties fully consistent with vigorous starburst galaxies at z>=3.5. The comoving number density of galaxies with stellar masses >= 5x10^10M_sun(a reasonable stellar mass completeness limit for our sample) is 2.6x10^-5Mpc^-3 (using the volume within 3.5<z<5), and the corresponding stellar mass density is ~2.9x10^6M_sunMpc^-3, or~3% of the local density above the same stellar mass limit.For the sub-sample of MIPS-undetected galaxies,we find a number density of ~0.97x10^-5Mpc^-3 and a stellar mass density of ~1.15x10^6M_sun Mpc^-3.[abridged]Comment: Accepted by A&A; 35 pages, 15 figures, references update

Locality-Sensitive Hashing with Margin Based Feature Selection

We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table