Efficient image retrieval using multi neural hash codes and bloom filters

• URL: http://arxiv.org/abs/2011.03234v2
• Date: Wed, 18 Nov 2020 12:09:08 GMT
• Title: Efficient image retrieval using multi neural hash codes and bloom filters
• Authors: Sourin Chakrabarti
• Abstract summary: This paper delivers an efficient and modified approach for image retrieval using multiple neural hash codes. It also limits the number of queries using bloom filters by identifying false positives beforehand.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval [0.46040036610482665]
  We propose a method for CBIR that captures both visual and semantic similarity using a visual hierarchy. The hierarchy is constructed by merging classes with overlapping features in the latent space of a deep neural network trained for classification. Our method achieves superior performance compared to the existing methods on image retrieval.
  arXiv  Detail & Related papers  (2023-08-16T15:23:14Z)
• MaskSearch: Querying Image Masks at Scale [60.82746984506577]
  MaskSearch is a system that focuses on accelerating queries over databases of image masks while guaranteeing the correctness of query results. Experiments with our prototype show that MaskSearch, using indexes approximately 5% of the compressed data size, accelerates individual queries by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2023-05-03T18:28:14Z)
• Image Completion via Dual-path Cooperative Filtering [17.62197747945094]
  We propose a predictive filtering method for restoring images based on the input scene. Deep feature-level semantic filtering is introduced to fill in missing information. Experiments on three challenging image completion datasets show that our proposed DCF outperforms state-of-art methods.
  arXiv  Detail & Related papers  (2023-04-30T03:54:53Z)
• Contextual Similarity Aggregation with Self-attention for Visual Re-ranking [96.55393026011811]
  We propose a visual re-ranking method by contextual similarity aggregation with self-attention. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method.
  arXiv  Detail & Related papers  (2021-10-26T06:20:31Z)
• SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition [31.714928102950594]
  We present a novel hybrid system that creates a high performance initial match hypothesis generator. Sequence descriptors are generated using a temporal convolutional network dubbed SeqNet. We then perform selective sequential score aggregation using shortlisted single image learnt descriptors to produce an overall place match hypothesis.
  arXiv  Detail & Related papers  (2021-02-23T10:32:10Z)
• Searching for Controllable Image Restoration Networks [57.23583915884236]
  Existing methods require separate inference through the entire network per each output. We propose a novel framework based on a neural architecture search technique that enables efficient generation of multiple imagery effects.
  arXiv  Detail & Related papers  (2020-12-21T10:08:18Z)
• Convolutional Neural Networks from Image Markers [62.997667081978825]
  Feature Learning from Image Markers (FLIM) was recently proposed to estimate convolutional filters, with no backpropagation, from strokes drawn by a user on very few images. This paper extends FLIM for fully connected layers and demonstrates it on different image classification problems. The results show that FLIM-based convolutional neural networks can outperform the same architecture trained from scratch by backpropagation.
  arXiv  Detail & Related papers  (2020-12-15T22:58:23Z)
• Learning to Compose Hypercolumns for Visual Correspondence [57.93635236871264]
  We introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
  arXiv  Detail & Related papers  (2020-07-21T04:03:22Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• CBIR using features derived by Deep Learning [0.0]
  In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. We propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem.
  arXiv  Detail & Related papers  (2020-02-13T21:26:32Z)
• Progressive Local Filter Pruning for Image Retrieval Acceleration [43.97722250091591]
  We propose a new Progressive Local Filter Pruning (PLFP) method for image retrieval acceleration. Specifically, layer by layer, we analyze the local geometric properties of each filter and select the one that can be replaced by the neighbors. In this way, the representation ability of the model is preserved.
  arXiv  Detail & Related papers  (2020-01-24T04:28:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.