Multimodal Fused Deep Learning Networks for Domain Specific Image Similarity Search

The exponential increase in data over the past fewyears,particularly in images,has led to more complex content since visual representation became the new norm.E-commerce and similar platforms maintain large image catalogues of their products.In image databases,searching and retrieving similar images is still a challenge,even though several image retrieval techniques have been proposed over the decade.Most of these techniques work well when querying general image databases.However,they often fail in domain-specific image databases,especially for datasets with low intraclass variance.This paper proposes a domain-specific image similarity search engine based on a fused deep learning network.The network is comprised of an improved object localization module,a classification module to narrow down search options and finally a feature extraction and similarity calculation module.The network features both an offline stage for indexing the dataset and an online stage for querying.The dataset used to evaluate the performance of the proposed network is a custom domain-specific dataset related to cosmetics packaging gathered from various online platforms.The proposed method addresses the intraclass variance problem with more precise object localization and the introduction of top result reranking based on object contours.Finally,quantitative and qualitative experiment results are presented,showing improved image similarity search performance.

Employment of predictive search algorithm in digital image correlation

A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.

Probabilistic hypergraph based hash codes for social image search

With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing(SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.

How spiders practice aggressive and Batesian mimicry

To understand communication, the interests of the sender and the receiver/s of signals should be considered sepa-rately. When our goal is to understand the adaptive significance of specific responses to specific signals by the receiver, questions about signal information are useful. However, when our goal is to understand the adaptive significance to the sender of generating a signal, it may be better to envisage the receiver＇s response to signals as part of the sender＇s extended phenotype. By making signals, a sender interfaces with the receiver＇s model of the world and indirectly manipulates its behaviour. This is especially clear in cases of mimicry, where animals use deceptive signals that indirectly manipulate the behaviour of receivers. Many animals adopt Batesian mimicry to deceive their predators, or aggressive mimicry to deceive their prey. We review examples from the lite-rature on spiders to illustrate how these phenomena, traditionally thought of as distinct, can become entangled in a web of lies .

Comparisons of JOULE 1 rocket thermospheric wind observations in high latitudes with GITM simulations

4 Summary and conclusion The JOULE sounding rocket 1 experiment was carried out at Poker Flat Research Range in Alaska around 1200 UT on March 27th,2003 with two instrumented rockets and one chemical tracer rocket.From the released TMA trails,neutral wind measurements from approximately 85 to 160 km altitude showed a vertically propagating wave and a jet structure around 120 km altitude.Large shears appeared at the bottom side of the jet with Richardson numbers close to or smaller than the critical value of 0.25,which implies the possible existence of Kelvin-Helmholtz instabilities caused by the vertical shear in the fast flows.