Efficient image retrieval using multi neural hash codes and bloom filters

This paper aims to deliver an efficient and modified approach for image retrieval using multiple neural hash codes and limiting the number of queries using bloom filters by identifying false positives beforehand. Traditional approaches involving neural networks for image retrieval tasks tend to use higher layers for feature extraction. But it has been seen that the activations of lower layers have proven to be more effective in a number of scenarios. In our approach, we have leveraged the use of local deep convolutional neural networks which combines the powers of both the features of lower and higher layers for creating feature maps which are then compressed using PCA and fed to a bloom filter after binary sequencing using a modified multi k-means approach. The feature maps obtained are further used in the image retrieval process in a hierarchical coarse-to-fine manner by first comparing the images in the higher layers for semantically similar images and then gradually moving towards the lower layers searching for structural similarities. While searching, the neural hashes for the query image are again calculated and queried in the bloom filter which tells us whether the query image is absent in the set or maybe present. If the bloom filter doesn't necessarily rule out the query, then it goes into the image retrieval process. This approach can be particularly helpful in cases where the image store is distributed since the approach supports parallel querying.Comment: 2020 IEEE International Conference for Innovation in Technology. Asian Journal for Convergence in Technology(AJCT) Volume VI Issue II

Nearest Neighbors Using Compact Sparse Codes

International audienceIn this paper, we propose a novel scheme for approximate nearest neighbor (ANN) retrieval based on dictionary learning and sparse coding. Our key innovation is to build compact codes, dubbed SpANN codes, using the active set of sparse coded data. These codes are then used to index an inverted file table for fast retrieval. The active sets are often found to be sensitive to small differences among data points, resulting in only near duplicate retrieval. We show that this sensitivity is related to the coherence of the dictionary; small coherence resulting in better retrieval. To this end, we propose a novel dictionary learning formulation with incoherence constraints and an efficient method to solve it. Experiments are conducted on two state-of-the-art computer vision datasets with 1M data points and show an order of magnitude improvement in retrieval accuracy without sacrificing memory and query time compared to the state-of-the-art methods

Efficient Gauss Elimination for Near-Quadratic Matrices with One Short Random Block per Row, with Applications

In this paper we identify a new class of sparse near-quadratic random Boolean matrices that have full row rank over F_2 = {0,1} with high probability and can be transformed into echelon form in almost linear time by a simple version of Gauss elimination. The random matrix with dimensions n(1-epsilon) x n is generated as follows: In each row, identify a block of length L = O((log n)/epsilon) at a random position. The entries outside the block are 0, the entries inside the block are given by fair coin tosses. Sorting the rows according to the positions of the blocks transforms the matrix into a kind of band matrix, on which, as it turns out, Gauss elimination works very efficiently with high probability. For the proof, the effects of Gauss elimination are interpreted as a ("coin-flipping") variant of Robin Hood hashing, whose behaviour can be captured in terms of a simple Markov model from queuing theory. Bounds for expected construction time and high success probability follow from results in this area. They readily extend to larger finite fields in place of F_2. By employing hashing, this matrix family leads to a new implementation of a retrieval data structure, which represents an arbitrary function f: S -> {0,1} for some set S of m = (1-epsilon)n keys. It requires m/(1-epsilon) bits of space, construction takes O(m/epsilon^2) expected time on a word RAM, while queries take O(1/epsilon) time and access only one contiguous segment of O((log m)/epsilon) bits in the representation (O(1/epsilon) consecutive words on a word RAM). The method is readily implemented and highly practical, and it is competitive with state-of-the-art methods. In a more theoretical variant, which works only for unrealistically large S, we can even achieve construction time O(m/epsilon) and query time O(1), accessing O(1) contiguous memory words for a query. By well-established methods the retrieval data structure leads to efficient constructions of (static) perfect hash functions and (static) Bloom filters with almost optimal space and very local storage access patterns for queries

A system for large-scale image and video retrieval on everyday scenes

There has been a growing amount of multimedia data generated on the web todayin terms of size and diversity. This has made accurate content retrieval with these large and complex collections of data a challenging problem. Motivated by the need for systems that can enable scalable and efficient search, we propose QIK (Querying Images Using Contextual Knowledge). QIK leverages advances in deep learning (DL) and natural language processing (NLP) for scene understanding to enable large-scale multimedia retrieval on everyday scenes with common objects. The system consists of three major components: Indexer, Query Processor, and Video Processor. Given an image, the Indexer performs probabilistic image understanding (PIU). The PIU generated consists of the most probable captions, parsed and represented by tree structures using NLP techniques, and detected objects. The PIU's are stored and indexed in a database system. For a query image, the Query Processor generates the most probable caption and parses it into the corresponding tree structure. Then an optimized tree-pattern query is constructed and executed on the database to retrieve a set of candidate images. The candidate images fetched are ranked using the tree-edit distance metric computed on the tree structures. Given a video, the Video Processor extracts a sequence of key scenes that are posed to the Query Processor to retrieve a set of candidate scenes. The candidate scene parse trees corresponding to a video are extracted and are ranked based on the number of matching scenes. We evaluated the performance of our system for large-scale image and video retrieval tasks on datasets containing everyday scenes and observed that our system could outperform state-ofthe- art techniques in terms of mean average precision.Includes bibliographical references

Delta bloom filter compression using stochastic learning-based weak estimation

Substantial research has been done, and sill continues, for reducing the bandwidth requirement and for reliable access to the data, stored and transmitted, in a space efficient manner. Bloom filters and their variants have achieved wide spread acceptability in various fields due to their ability to satisfy these requirements. As this need has increased, especially, for the applications which require heavy use of the transmission bandwidth, distributed computing environment for the databases or the proxy servers, and even the applications which are sensitive to the access to the information with frequent modifications, this thesis proposes a solution in the form of compressed delta Bloom filter. This thesis proposes delta Bloom filter compression, using stochastic learning-based weak estimation and prediction with partial matching to achieve the goal of lossless compression with high compression gain for reducing the large data transferred frequently