Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.Comment: Accepted to IEEE ICIP 201

Selective Deep Convolutional Features for Image Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201

From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations. Regarding the former requirement, Convolutional Neural Network is proven to be a very powerful tool to extract highly discriminative local descriptors for effective image search. Additionally, to further improve the discriminative power of the descriptors, recent works adopt fine-tuned strategies. In this article, taking a different approach, we propose a novel, computationally efficient, and competitive framework. Specifically, we first propose various strategies to compute masks, namely, SIFT-masks , SUM-mask , and MAX-mask , to select a representative subset of local convolutional features and eliminate redundant features. Our in-depth analyses demonstrate that proposed masking schemes are effective to address the burstiness drawback and improve retrieval accuracy. Second, we propose to employ recent embedding and aggregating methods that can significantly boost the feature discriminability. Regarding the computation and storage efficiency, we include a hashing module to produce very compact binary image representations. Extensive experiments on six image retrieval benchmarks demonstrate that our proposed framework achieves the state-of-the-art retrieval performances. </jats:p

Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation

Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-to-end deep learning approach for the task, in which the network is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular, to deal with the non-smoothness of binary constraints, we propose a novel pairwise constrained loss function, which simultaneously encodes the distances between pairs of hash codes, and the binary quantization error. In order to train the network with the proposed loss function, we propose an efficient parameter learning algorithm. In addition, to provide similar / dissimilar training images to train the network, we exploit 3D models reconstructed from unlabelled images for automatic generation of enormous training image pairs. The extensive experiments on image retrieval benchmark datasets demonstrate the improvements of the proposed method over the state-of-the-art compact representation methods on the image retrieval problem.Comment: Accepted to WACV 201

Species composition, abundance and biomass distribution of zoobenthos in Vietnamese waters

The benthic invertebrate (zoobenthos) fauna in Vietnamese seawaters was surveyed in April - May, 1999. Zoobenthos specimen were sampled by Smith-McIntyre grab on 38 stations and 180 species were recorded and composed of 5 major groups: Polychaeta, Crustacea, Mollusca, Echinodermata and others. The total of density and biomass zoobenthos in Vietnamese seawaters was 156.7 ind/m2 and 5943.0 mg/m2 respectively. Polychaeta and Mollusca were groups with the highest abundance in every cases considered. The remaining groups of zoobenthos such as Crustacea and Echinodermata which were lower in abundance but higher in biomass. There was a remarkable variation of zoobenthos both in species composition and density with the depth, substrate and spatial distribution. Abundance was higher in some subjects considered such as: in depth of 0 - 60m or in types of sandy components or in the Tonkin gulf and the Southeast regions. All diversity indices shown that water quantity in Vietnamese sea offshore in survey time was just satisfactory and good