Search results “Hamming weight in visual cryptography”
Query adaptive image search using hash codes
ABSTRACT: Scalable image search based on visual similarity has been an active topic of research in recent years. State-of-the-art solutions often use hashing methods to embed high-dimensional image features into Hamming space, where search can be performed in real-time based on Hamming distance of compact hash codes. Unlike traditional metrics (e.g., Euclidean) that offer continuous distances, the Hamming distances are discrete integer values. As a consequence, there are often a large number of images sharing equal Hamming distances to a query, which largely hurts search results where fine-grained ranking is very important. This paper introduces an approach that enables query-adaptive ranking of the returned images with equal Hamming distances to the queries. This is achieved by firstly offline learning bitwise weights of the hash codes for a diverse set of predefined semantic concept classes. We formulate the weight learning process as a quadratic programming problem that minimizes intra-class distance while preserving inter-class relationship captured by original raw image features. Query-adaptive weights are then computed online by evaluating the proximity between a query and the semantic concept classes. With the query-adaptive bitwise weights, returned images can be easily ordered by weighted Hamming distance at a finer-grained hash code level rather than the original Hamming distance level. Experiments on a Flickr image dataset show clear improvements from our proposed approach. EXISTING SYSTEM: While traditional image search engines heavily rely on textual words associated to the images, scalable content-based search is receiving increasing attention. Apart from providing better image search experience for ordinary Web users, large-scale similar image search has also been demonstrated to be very helpful for solving a number of very hard problems in computer vision and multimedia such as image categorization. DISADVANTAGES OF EXISTING SYSTEM: An efficient search mechanism is critical since existing image features are mostly of high dimensions and current image databases are huge, on top of which exhaustively comparing a query with every database sample is computationally prohibitive. PROPOSED SYSTEM: In this work we represent images using the popular bag-of-visual-words (BoW) framework, where local invariant image descriptors (e.g., SIFT) are extracted and quantized based on a set of visual words. The BoW features are then embedded into compact hash codes for efficient search. For this, we consider state-of-the-art techniques including semi-supervised hashing and semantic hashing with deep belief networks. Hashing is preferable over tree-based indexing structures (e.g., kd-tree) as it generally requires greatly reduced memory and also works better for high-dimensional samples. With the hash codes, image similarity can be efficiently measured (using logical XOR operations) in Hamming space by Hamming distance, an integer value obtained by counting the number of bits at which the binary values are different. In large scale applications, the dimension of Hamming space is usually set as a small number (e.g., less than a hundred) to reduce memory cost and avoid low recall. ADVANTAGES OF PROPOSED SYSTEM: The main contribution of this paper is the proposal of a novel approach that computes query-adaptive weights for each bit of the hash codes, which has two main advantages. First, images can be ranked on a finer-grained hash code level since—with the bitwise weights—each hash code is expected to have a unique similarity to the queries. In other words, we can push the resolution of ranking from (traditional Hamming distance level) up to (hash code level1). Second, contrary to using a single set of weights for all the queries, our approach tailors a different and more suitable set of weights for each query.
Views: 513 tchnopundits
Optimised Multiplication Architectures for Accelerating Fully Homomorphic Encryption (0916 Chinese)
Large integer multiplication is a major performance bottleneck in fully homomorphic encryption (FHE) schemes over the integers. In this paper two optimised multiplier architectures for large integer multiplication are proposed. The first of these is a low-latency hardware architecture of an integer-FFT multiplier. Secondly, the use of low Hamming weight (LHW) parameters is applied to create a novel hardware architecture for large integer multiplication in integer-based FHE schemes. The proposed architectures are implemented, verified and compared on the Xilinx Virtex-7 FPGA platform. Finally, the proposed implementations are employed to evaluate the large multiplication in the encryption step of FHE over the integers. The analysis shows a speed improvement factor of up to 26.2 for the low-latency design compared to the corresponding original integer-based FHE software implementation. When the proposed LHW architecture is combined with the low-latency integer-FFT accelerator to evaluate a single FHE encryption operation, the performance results show that a speed improvement by a factor of approximately 130 is possible.
Mod-01 Lec-24 Lecture-24 Biometrics
Biometrics by Prof. Phalguni Gupta, Department of Computer Science and Engineering, IIT Kanpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 602 nptelhrd
Timeline of United States inventions (1946–91) | Wikipedia audio article
This is an audio version of the Wikipedia Article: Timeline of United States inventions (1946–91) Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= A timeline of United States inventions (1946–1991) encompasses the ingenuity and innovative advancements of the United States within a historical context, dating from the era of the Cold War, which have been achieved by inventors who are either native-born or naturalized citizens of the United States. Copyright protection secures a person's right to his or her first-to-invent claim of the original invention in question, highlighted in Article I, Section 8, Clause 8 of the United States Constitution which gives the following enumerated power to the United States Congress: In 1641, the first patent in North America was issued to Samuel Winslow by the General Court of Massachusetts for a new method of making salt. On April 10, 1790, President George Washington signed the Patent Act of 1790 (1 Stat. 109) into law which proclaimed that patents were to be authorized for "any useful art, manufacture, engine, machine, or device, or any improvement therein not before known or used." On July 31, 1790, Samuel Hopkins of Pittsford, Vermont became the first person in the United States to file and to be granted a patent for an improved method of "Making Pot and Pearl Ashes." The Patent Act of 1836 (Ch. 357, 5 Stat. 117) further clarified United States patent law to the extent of establishing a patent office where patent applications are filed, processed, and granted, contingent upon the language and scope of the claimant's invention, for a patent term of 14 years with an extension of up to an additional 7 years. However, the Uruguay Round Agreements Act of 1994 (URAA) changed the patent term in the United States to a total of 20 years, effective for patent applications filed on or after June 8, 1995, thus bringing United States patent law further into conformity with international patent law. The modern-day provisions of the law applied to inventions are laid out in Title 35 of the United States Code (Ch. 950, sec. 1, 66 Stat. 792). From 1836 to 2011, the United States Patent and Trademark Office (USPTO) has granted a total of 7,861,317 patents relating to several well-known inventions appearing throughout the timeline below. Some examples of patented inventions between the years 1946 and 1991 include William Shockley's transistor (1947), John Blankenbaker's personal computer (1971), Vinton Cerf's and Robert Kahn's Internet protocol/TCP (1973), and Martin Cooper's mobile phone (1973).
Views: 81 wikipedia tts

Plant lab report example
Aed powerpoint presentation uk
Office coordinator cover letter real
Sales visit report formats
Dissertation paypal