Ptolemaic Indexing

This paper discusses a new family of bounds for use in similarity search, related to those used in metric indexing, but based on Ptolemy's inequality, rather than the metric axioms. Ptolemy's inequality holds for the well-known Euclidean distance, but is also shown here to hold for quadratic form metrics in general, with Mahalanobis distance as an important special case. The inequality is examined empirically on both synthetic and real-world data sets and is also found to hold approximately, with a very low degree of error, for important distances such as the angular pseudometric and several Lp norms. Indexing experiments demonstrate a highly increased filtering power compared to existing, triangular methods. It is also shown that combining the Ptolemaic and triangular filtering can lead to better results than using either approach on its own

Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces

This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library

Substance Classification By Legend Rooted Vector Gap

Unlike tree indexes adopted in current business, our index is less receptive to scaling up dimensions and scales well with multi-dimensional data. Unsolicited candidates are cut according to distances between MBR points or keywords and also with the best diameter found. NKS queries are useful for many applications, for example, discussing images in social systems, searching for graphic patterns, searching for geolocation in GIS systems, etc. We produce accurate shape as well as approx shape of formula. In this paper, we consider keyword-bearing objects thus baked into a vector space. Keyword-based searches in text-rich multi-dimensional datasets facilitate many new applications and tools. From these datasets, we study queries that require the smallest point categories that satisfy the set of proven keywords. Our experimental results on real and synthetic datasets show that ProMiSH has up to 60 chances of acceleration compared to modern column-based technologies. We recommend a unique method known as ProMiSH, which uses random projection and hash-based index structures and delivers high scalability and acceleration. We are conducting extensive pilot studies to demonstrate the performance of the proposed technologies