Biblio

Currently, one method to deal with the storage and computation of multimedia retrieval applications is an approximate nearest neighbor (ANN) search. Hashing algorithms and Vector quantization (VQ) are widely used in ANN search. So, K-mean clustering is a method of VQ that can solve those problems. With the increasing growth of multimedia data such as text view, image view, video view, audio view, and 3D view. Thus, it is a reason that why multimedia retrieval is very important. We can retrieve the results of each media type by inputting a query of that type. Even though many hashing algorithms and VQ techniques are proposed to produce a compact or short binary codes. In the real-time purposes the exhaustive search is impractical, and Hamming distance computation in the Hamming space suffers inaccurate results. The challenge of this paper is focusing on how to learn multimedia raw data or features representation to search on each media type for multimedia retrieval. So we propose a new search method that utilizes K-mean hash codes by computing the probability of a cluster in the index code. The proposed employs the index code from the K-mean cluster number that is converted to hash code. The inverted index table is constructed basing on the K-mean hash code. Then we can improve the original K-mean index accuracy and efficiency by learning a deep neural network (DNN). We performed the experiments on four benchmark multimedia datasets to retrieve each view such as 3D, image, video, text, and audio, where hash codes are produced by K-mean clustering methods. Our results show the effectiveness boost the performance on the baseline (exhaustive search).

In recent times cloud services are used widely and due to which there are so many attacks on the cloud devices. One of the major attacks is DDos (distributed denial-of-service) -attack which mainly targeted the Memcached which is a caching system developed for speeding the websites and the networks through Memcached's database. The DDoS attack tries to destroy the database by creating a flood of internet traffic at the targeted server end. Attackers send the spoofing applications to the vulnerable UDP Memcached server which even manipulate the legitimate identity of the sender. In this work, we have proposed a vector quantization approach based on a supervised deep learning approach to detect the Memcached attack performed by the use of malicious firmware on different types of Cloud attached devices. This vector quantization approach detects the DDoas attack performed by malicious firmware on the different types of cloud devices and this also classifies the applications which are vulnerable to attack based on cloud-The Hackbeased services. The result computed during the testing shows the 98.2 % as legally positive and 0.034% as falsely negative.

A fundamental recurring task in many machine learning applications is the search for the Nearest Neighbor in high dimensional metric spaces. Towards answering queries in large scale problems, state-of-the-art methods employ Approximate Nearest Neighbors (ANN) search, a search that returns the nearest neighbor with high probability, as well as techniques that compress the dataset. Product-Quantization (PQ) based ANN search methods have demonstrated state-of-the-art performance in several problems, including classification, regression and information retrieval. The dataset is encoded into a Cartesian product of multiple low-dimensional codebooks, enabling faster search and higher compression. Being intrinsically parallel, PQ-based ANN search approaches are amendable for hardware acceleration. This paper proposes a novel Hierarchical PQ (HPQ) based ANN search method as well as an FPGA-tailored architecture for its implementation that outperforms current state of the art systems. HPQ gradually refines the search space, reducing the number of data compares and enabling a pipelined search. The mapping of the architecture on a Stratix 10 FPGA device demonstrates over ×250 speedups over current state-of-the-art systems, opening the space for addressing larger datasets and/or improving the query times of current systems.

A main issue in approximate nearest neighbor search is to achieve an excellent tradeoff between search accuracy and computation cost. In this paper, we address this issue by leveraging k-nearest neighbor graph and hill-climbing to accelerate vector quantization in the query assignment process. A modified hill-climbing algorithm is proposed to traverse k-nearest neighbor graph to find closest centroids for a query, rather than calculating the query distances to all centroids. Instead of using random seeds in the original hill-climbing algorithm, we generate high-quality seeds based on the hashing technique. It can boost the query assignment efficiency due to a better start-up in hill-climbing. We evaluate the experiment on the benchmarks of SIFT1M and GIST1M datasets, and show the proposed hashing-based seed generation effectively improves the search performance.