Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases

Ercoli, Simone; Bertini, Marco; Bimbo, Alberto Del

doi:10.1109/tmm.2017.2697824

Cited by 41 publications

(13 citation statements)

References 52 publications

(157 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sparse Product Quantization (SPQ) [39] encodes the high-dimensional feature vectors into sparse representation by decomposing the feature space into a Cartesian product of low-dimensional subspaces and quantizing each subspace via K-means clustering, and the sparse representations are optimized by minimizing their quantization errors. [40] proposes to learn compact hash code by computing a sort of soft assignment within the k-means framework, which is called "multi-k-means", to void the expensive memory and computing requirements. Latent Semantic Minimal Hashing (LSMH) [41] refines latent semantic feature embedding in the image feature to refine original feature based on matrix decomposition, and a minimum encoding loss is combined with latent semantic feature learning process simultaneously to get discriminative obtained binary codes.…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

Zheng

Zou

Lin

et al. 2020

IEEE Trans. Multimedia

126

View full text Add to dashboard Cite

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional featurelearning methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, an improved deep hashing method is proposed to enhance the ability of multi-label image retrieval. We introduce a pairwise quantified similarity calculated on the normalized semantic labels. Based on this, we divide the pairwise similarity into two situations -'hard similarity' and 'soft similarity', where cross-entropy loss and mean square error loss are adapted respectively for more robust feature learning and hash coding. Experiments on four popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.Index Terms-image retrieval, convolutional neural network, semantic label, pairwise similarity, deep hashing.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

Zheng

Zou

Lin

et al. 2020

IEEE Trans. Multimedia

126

View full text Add to dashboard Cite

show abstract

“…Ercoli et al [32] via obtaining multi k-means technique have premeditated hash codes and used them for repossession of visual descriptors. Krichevsky [16] on the CIFAR-10 dataset uses a 2-layer Convolutional Deep Belief Network (CDBN).…”

Section: Related Workmentioning

confidence: 99%

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Ali

Pei

et al. 2020

Symmetry

View full text Add to dashboard Cite

Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

show abstract

“…Ercoli et al [11] propose a quantization process that is based on the distance of a feature vector to precomputed cluster centroids which are obtained by k-means. They evaluate the threshold measures as geometric mean, arithmetic mean, and n th -nearest distance.…”

Section: Related Workmentioning

confidence: 99%

Binary convolutional neural network features off-the-shelf for image to video linking in endoscopic multimedia databases

Petscharnig

Schöffmann

2018

Multimed Tools Appl

View full text Add to dashboard Cite

With a rigorous long-term archival of endoscopic surgeries, vast amounts of video and image data accumulate. Surgeons are not able to spend their valuable time to manually search within endoscopic multimedia databases (EMDBs) or manually maintain links to interesting sections in order to quickly retrieve relevant surgery sections. Enabling the surgeons to quickly access the relevant surgery scenes, we utilize the fact that surgeons record external images additionally to the surgery video and aim to link them to the appropriate video sequence in the EMDB using a query-by-example approach. We propose binary Convolutional Neural Network (CNN) features off-the-shelf and compare them to several baselines: pixel-based comparison (PSNR), image structure comparison (SSIM), hand-crafted global features (CEDD and feature signatures), as well as CNN baselines Histograms of Class Confidences (HoCC) and Neural Codes (NC). For evaluation, we use 5.5 h of endoscopic video material and 69 query images selected by medical experts and compare the performance of the aforementioned image mathing methods in terms of video hit rate and distance to the true playback time stamp (PTS) for correct video predictions. Our evaluation shows that binary CNN features are compact, yet powerful image descriptors for retrieval in the endoscopic imaging domain. They are able to maintain state-of-the-art performance, while providing the benefit of low storage space requirements and hence provide the best compromise.

show abstract

Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases

Cited by 41 publications

References 52 publications

Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Binary convolutional neural network features off-the-shelf for image to video linking in endoscopic multimedia databases

Contact Info

Product

Resources

About