Strongly Constrained Discrete Hashing
نویسندگان
چکیده
منابع مشابه
Discrete Graph Hashing
Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases. In particular, learning based hashing has received considerable attention due to its appealing storage and search efficiency. However, the performance of most unsupervised learning based hashing methods deteriorates rapidly as the hash code length increases. We argue that the degraded performance ...
متن کاملDeep Supervised Discrete Hashing
With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this p...
متن کاملDeep Discrete Supervised Hashing
Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, util...
متن کاملStrongly History Independent Hashing with Deletion
We present a strongly history independent (SHI) hash table that is fast, space efficient, and supports deletions. A hash table that supports deletions is SHI if it has a canonical memory representation up to randomness. That is, the string of random bits and current hash table contents (the set of (key, object) pairs in the hash table) uniquely determine its layout in memory, independently of t...
متن کاملStrongly Universal String Hashing is Fast
We present fast strongly universal string hashing families: they can process data at a rate of 0.2 CPU cycle per byte. Maybe surprisingly, we find that these families— though they requires a large buffer of random numbers—are often faster than popular hash functions with weaker theoretical guarantees. Moreover, conventional wisdom is that hash functions with fewer multiplications are faster. Ye...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2020
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2020.2963952