VIABILITY OF TIME-MEMORY TRADE-OFFS IN LARGE DATA SETS

The main hypothesis of this paper is whether compression performance – both hardware and software – is at, approaching, or will ever reach a point where real-time compression of cached data in large data sets will be viable to improve hit ratios and overall throughput. The problem identified is: storage access is unable to keep up with application and user demands, and cache (RAM) is too small to contain full data sets. A literature review of several existing techniques discusses how storage IO is reduced or optimized to maximize the available performance of the storage medium. However, none of the techniques discovered preclude, or are mutually exclusive with, the hypothesis proposed herein. The methodology includes gauging three popular compressors which meet the criteria for viability: zlib, lz4, and zstd. Common storage devices are also benchmarked to establish costs for both IO and compression operations to help build charts and discover break-even points under various circumstances. The results indicate that modern CISC processors and compressors are already approaching tradeoff viability, and that FPGA and ASIC could potentially reduce all overhead by pipelining compression – nearly eliminating the cost portion of the tradeoff, leaving mostly benefit

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly