Set Of Similar Items Research Articles

Asymmetric Hash Code Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing learning as the current mainstream method has achieved satisfactory retrieval performance. On one hand, various deep neural networks are used to extract semantic features of remote sensing images. On the other hand, the hashing techniques are subsequently adopted to map the high-dimensional deep features to the low-dimensional binary codes. This kind of method attempts to learn one hash function for both the query and database samples in a symmetric way. However, with the number of database samples increasing, it is typically time-consuming to generate the hash codes of large-scale database images. In this article, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL generates the hash codes of query and database images in an asymmetric way. In more detail, the hash codes of query images are obtained by binarizing the output of the network, while the hash codes of database images are directly learned by solving the designed objective function. In addition, we combine the semantic information of each image and the similarity information of pairs of images as supervised information to train a deep hashing network, which improves the representation ability of deep features and hash codes. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weiweisong415/Demo_AHCL_for_TGRS2022</uri> .

Neural representations of ensemble coding in the occipital and parietal cortices

The human visual system is able to extract summary statistics from sets of similar items, but the underlying neural mechanism remains poorly understood. Using functional magnetic resonance imaging (fMRI) and an encoding model, we examined how the neural representation of ensemble coding is constructed by manipulating the task-relevance of ensemble features. We found a gradual increase in orientation-selective responses to the mean orientation of multiple stimuli along the visual hierarchy only when these orientations were task-relevant. Such responses to the ensemble orientation were present in the extrastriate area, V3, even when the mean orientation was not task-relevant, indicating that the ensemble representation can co-exist with the task-relevant individual feature representation. Ensemble orientations were also represented in frontal regions, but those representations were robust only when each mean orientation was linked to a motor response dimension. Together, our findings suggest that the neural representation of the ensemble percept is formed by pooling signals at multiple levels of the visual processing stream.

Measurement properties of the project-level Women's Empowerment in Agriculture Index.

Women's empowerment is a process that includes increases in intrinsic agency (power within); instrumental agency (power to); and collective agency (power with). We used baseline data from two studies-Targeting and Realigning Agriculture for Improved Nutrition (TRAIN) in Bangladesh and Building Resilience in Burkina Faso (BRB)-to assess the measurement properties of survey questions operationalizing selected dimensions of intrinsic, instrumental, and collective agency in the project-level Women's Empowerment in Agricultural Index (pro-WEAI). We applied unidimensional item-response models to question (item) sets to assess their measurement properties, and when possible, their cross-context measurement equivalence-a requirement of measures designed for cross-group comparisons. For intrinsic agency in the right to bodily integrity, measured with five attitudinal questions about intimate partner violence (IPV) against women, model assumptions of unidimensionality and local independence were met. Four items showed good model fit and measurement equivalence across TRAIN and BRB. For item sets designed to capture autonomy in income, intrinsic agency in livelihoods activities, and instrumental agency in: livelihoods activities, the sale or use of outputs, the use of income, and borrowing from financial services, model assumptions were not met, model fit was poor, and items generally were weakly related to the latent (unobserved) agency construct. For intrinsic and instrumental agency in livelihoods activities and for instrumental agency in the sale or use of outputs and in the use of income, items sets had similar precision along the latent-agency continuum, suggesting that similar item sets could be dropped without a loss of precision. IRT models for collective agency were not estimable because of low reported presence and membership in community groups. This analysis demonstrates the use of IRT methods to assess the measurement properties of item sets in pro-WEAI, and empowerment scales generally. Findings suggest that a shorter version of pro-WEAI can be developed that will improve its measurement properties. We recommend revisions to the pro-WEAI questionnaire and call for new measures of women's collective agency.

ELSA

Sentence-based summarization aims at extracting concise summaries of collections of textual documents. Summaries consist of a worthwhile subset of document sentences. The most effective multilingual strategies rely on Latent Semantic Analysis (LSA) and on frequent itemset mining, respectively. LSA-based summarizers pick the document sentences that cover the most important concepts. Concepts are modeled as combinations of single-document terms and are derived from a term-by-sentence matrix by exploiting Singular Value Decomposition (SVD). Itemset-based summarizers pick the sentences that contain the largest number of frequent itemsets, which represent combinations of frequently co-occurring terms. The main drawbacks of existing approaches are (i) the inability of LSA to consider the correlation between combinations of multiple-document terms and the underlying concepts, (ii) the inherent redundancy of frequent itemsets because similar itemsets may be related to the same concept, and (iii) the inability of itemset-based summarizers to correlate itemsets with the underlying document concepts. To overcome the issues of both of the abovementioned algorithms, we propose a new summarization approach that exploits frequent itemsets to describe all of the latent concepts covered by the documents under analysis and LSA to reduce the potentially redundant set of itemsets to a compact set of uncorrelated concepts. The summarizer selects the sentences that cover the latent concepts with minimal redundancy. We tested the summarization algorithm on both multilingual and English-language benchmark document collections. The proposed approach performed significantly better than both itemset- and LSA-based summarizers, and better than most of the other state-of-the-art approaches.

Correcting "confusability regions" in face morphs.

The visual system represents summary statistical information from a set of similar items, a phenomenon known as ensemble perception. In exploring various ensemble domains (e.g., orientation, color, facial expression), researchers have often employed the method of continuous report, in which observers select their responses from a gradually changing morph sequence. However, given their current implementation, some face morphs unintentionally introduce noise into the ensemble measurement. Specifically, some facial expressions on the morph wheel appear perceptually similar even though they are far apart in stimulus space. For instance, in a morph wheel of happy-sad-angry-happy expressions, an expression between happy and sad may not be discriminable from an expression between sad and angry. Without accounting for this confusability, observer ability will be underestimated. In the present experiments we accounted for this by delineating the perceptual confusability of morphs of multiple expressions. In a two-alternative forced choice task, eight observers were asked to discriminate between anchor images (36 in total) and all 360 facial expressions on the morph wheel. The results were visualized on a "confusability matrix," depicting the morphs most likely to be confused for one another. The matrix revealed multiple confusable images between distant expressions on the morph wheel. By accounting for these "confusability regions," we demonstrated a significant improvement in performance estimation on a set of independent ensemble data, suggesting that high-level ensemble abilities may be better than has been previously thought. We also provide an alternative computational approach that may be used to determine potentially confusable stimuli in a given morph space.

Ensemble perception in autism spectrum disorder: Member-identification versus mean-discrimination.

To efficiently represent the outside world our brain compresses sets of similar items into a summarized representation, a phenomenon known as ensemble perception. While most studies on ensemble perception investigate this perceptual mechanism in typically developing (TD) adults, more recently, researchers studying perceptual organization in individuals with autism spectrum disorder (ASD) have turned their attention toward ensemble perception. The current study is the first to investigate the use of ensemble perception for size in children with and without ASD (N = 42, 8-16 years). We administered a pair of tasks pioneered by Ariely [2001] evaluating both member-identification and mean-discrimination. In addition, we varied the distribution types of our sets to allow a more detailed evaluation of task performance. Results show that, overall, both groups performed similarly in the member-identification task, a test of "local perception," and similarly in the mean identification task, a test of "gist perception." However, in both tasks performance of the TD group was affected more strongly by the degree of stimulus variability in the set, than performance of the ASD group. These findings indicate that both TD children and children with ASD use ensemble statistics to represent a set of similar items, illustrating the fundamental nature of ensemble coding in visual perception. Differences in sensitivity to stimulus variability between both groups are discussed in relation to recent theories of information processing in ASD (e.g., increased sampling, decreased priors, increased precision). Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1291-1299. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Sensory processing patterns predict the integration of information held in visual working memory.

Given the limited resources of visual working memory, multiple items may be remembered as an averaged group or ensemble. As a result, local information may be ill-defined, but these ensemble representations provide accurate diagnostics of the natural world by combining gist information with item-level information held in visual working memory. Some neurodevelopmental disorders are characterized by sensory processing profiles that predispose individuals to avoid or seek-out sensory stimulation, fundamentally altering their perceptual experience. Here, we report such processing styles will affect the computation of ensemble statistics in the general population. We identified stable adult sensory processing patterns to demonstrate that individuals with low sensory thresholds who show a greater proclivity to engage in active response strategies to prevent sensory overstimulation are less likely to integrate mean size information across a set of similar items and are therefore more likely to be biased away from the mean size representation of an ensemble display. We therefore propose the study of ensemble processing should extend beyond the statistics of the display, and should also consider the statistics of the observer. (PsycINFO Database Record

Mass of Short Texts Clustering and Topic Extraction Based on Frequent Itemsets

Short texts generated in social media have the characteristics of volume ,velocity ,low quality and variety ,thus make the vector‐space‐based clustering methods face the challenges of high‐dimensions ,features sparsity and noisy disturbing .In this paper ,we propose a short texts clustering and topic extraction (STC‐TE) framework based on the frequent itemsets mined from the texts .This framework firstly studies the impact of multi‐features on the short texts’ quality . Then ,a large amount of frequent itemsets are dug out from the high quality short text set via setting a low support level ,and a similar itemsets filtering strategy is devised to discard most of the unimportant frequent itemsets .Furthermore ,based on the frequent itemsets similarity evaluated by relevant texts ,we proposed a cluster self‐adaptive spectral clustering (CSA_SC ) algorithm to form the itemsets into different topic clusters .At last ,the large‐scale of short texts are classified into associated clusters according to the topic words extracted from the frequent itemset clusters .The framework is tested on one million of SinaWeibo dataset to evaluate the performance of the important frequent itemset selection and clustering ,the topic words extraction ,and the large scale of short texts classification . Experimental results show that the STC‐TE framework can achieve topic extraction and large‐scale short texts clustering with high accuracy .

Viewers extract the mean from images of the same person: A route to face learning.

Research on ensemble encoding has found that viewers extract summary information from sets of similar items. When shown a set of four faces of different people, viewers merge identity information from the exemplars into a representation of the set average. Here, we presented sets containing unconstrained images of the same identity. In response to a subsequent probe, viewers recognized the exemplars accurately. However, they also reported having seen a merged average of these images. Importantly, viewers reported seeing the matching average of the set (the average of the four presented images) more often than a nonmatching average (an average of four other images of the same identity). These results were consistent for both simultaneous and sequential presentation of the sets. Our findings support previous research suggesting that viewers form representations of both the exemplars and the set average. Given the unconstrained nature of the photographs, we also provide further evidence that the average representation is invariant to several high-level characteristics.

Can the humped animal's knee conceal its name? Commentary on: "The roles of shared vs. distinctive conceptual features in lexical access".

Can the humped animal's knee conceal its name? Commentary on: "The roles of shared vs. distinctive conceptual features in lexical access".

Ensemble crowd perception: a viewpoint-invariant mechanism to represent average crowd identity.

Individuals can rapidly and precisely judge the average of a set of similar items, including both low-level (Ariely, 2001) and high-level objects (Haberman & Whitney, 2007). However, to date, it is unclear whether ensemble perception is based on viewpoint-invariant object representations. Here, we tested this question by presenting participants with crowds of sequentially presented faces. The number of faces in each crowd and the viewpoint of each face varied from trial to trial. This design required participants to integrate information from multiple viewpoints into one ensemble percept. Participants reported the mean identity of crowds (e.g., family resemblance) using an adjustable, forward-oriented test face. Our results showed that participants accurately perceived the mean crowd identity even when required to incorporate information across multiple face orientations. Control experiments showed that the precision of ensemble coding was not solely dependent on the length of time participants viewed the crowd. Moreover, control analyses demonstrated that observers did not simply sample a subset of faces in the crowd but rather integrated many faces into their estimates of average crowd identity. These results demonstrate that ensemble perception can operate at the highest levels of object recognition after 3-D viewpoint-invariant faces are represented.

Set-fit effects in choice.

In 4 experiments, we investigate how the "fit" of an item with a set of similar items affects choice. We find that people have a notion of a set that "fits" together--one where all items are the same, or all items differ, on salient attributes. One consequence of this notion is that in addition to preferences over the set's individual items, choice reflects set-fit. This leads to predictable shifts in preferences, sometimes even resulting in people choosing normatively inferior options over superior ones.

Reversed better-than-average effect in direct comparisons of nonsocial stimuli depends on the set size

Studies on direct comparative judgments typically show that, for items that are positively evaluated, a single item randomly drawn from a larger set of similar items tends to be judged as better than average (the BTA effect). However, Windschitl, Conybeare, and Krizan (2008) demonstrated that, under timing conditions that do not favor focusing attention on the single item, the reversal of the BTA effect occurs. We report two experiments showing that the magnitude of the reversed BTA effect increases as a function of the size of a multiitem referent with which a single item target is compared. Specifically, in direct comparative judgments of the attractiveness of positively evaluated objects (nice-looking cloth buttons, attractive buildings, or cupcakes), underestimation of the attractiveness of singletons, as compared with a multiitem set (reversed BTA effect), increased with the increased set size. Analysis of absolute judgments obtained for singletons and for small and large multiitem sets suggests that, for attractive stimuli, both the reversed BTA effect in comparative judgments and its sensitivity to set size occur as a result of a positive relationship between set size and perceived attractiveness in absolute judgments.

Computing similarity between items in a digital library of cultural heritage

Large amounts of cultural heritage content have now been digitized and are available in digital libraries. However, these are often unstructured and difficult to navigate. Automatic techniques for identifying similar items in these collections could be used to improve navigation since it would allow items that are implicitly connected to be linked together and allow sets of similar items to be clustered. Europeana is a large digital library containing more than 20 million digital objects from a set of cultural heritage providers throughout Europe. The diverse nature of this collection means that the items do not have standard metadata to assist navigation. A range of methods for computing the similarity between pairs of texts are applied to metadata records in Europeana in order to estimate the similarity between items. Various methods for computing similarity have been proposed and can be classified into two main approaches: (1) knowledge-based, which make use of external knowledge sources and (2) corpus-based approaches, which rely on analyzing the frequency distributions of words in documents. Both techniques are evaluated against manual judgements obtained for this study and a multiple-choice test created from manually generated categories in cultural heritage collections. We find that a combination of corpus and knowledge-based approaches provide the best results in both experiments.

Size and emotion averaging: Costs of dividing attention after all.

Perceptual averaging is a process by which sets of similar items are represented by summary statistics such as their average size, luminance, or orientation. Researchers have argued that this process is automatic, able to be carried out without interference from concurrent processing. Here, we challenge this conclusion and demonstrate a reliable cost of computing the mean size of circles distinguished by colour (Experiments 1 and 2) and the mean emotionality of faces distinguished by sex (Experiment 3). We also test the viability of two strategies that could have allowed observers to guess the correct response without computing the average size or emotionality of both sets concurrently. We conclude that although two means can be computed concurrently, doing so incurs a cost of dividing attention.

Rapid averaging? Not so fast!

Previous research suggests that sets of similar items are represented using a rapid averaging mechanism that automatically extracts statistical properties within 50 ms. However, typically in these studies, displays are not masked, so it is possible that the sets are available for longer than this duration. In the present study, using masked displays, we (a) tested a newly proposed strategy for extracting the mean size of a set of circles, and (b) more precisely evaluated the time course of rapid averaging. The results indicate that when viewing conditions are poor, performance can be explained by assuming that observers rely on information from previous trials. In this study, observers required at least a 200-ms exposure time in order to derive the average size of a set of circles without relying on information from previously-viewed sets, suggesting that rapid averaging is not as fast as previously assumed and, therefore, that it may not be an automatic process.

The visual system discounts emotional deviants when extracting average expression

There has been a recent surge in the study of ensemble coding, the idea that the visual system represents a set of similar items using summary statistics (Alvarez & Oliva, 2008; Ariely, 2001; Chong & Treisman, 2003; Parkes, Lund, Angelucci, Solomon, & Morgan, 2001). We previously demonstrated that this ability extends to faces and thus requires a high level of object processing (Haberman & Whitney, 2007, 2009). Recent debate has centered on the nature of the summary representation of size (e.g., Myczek & Simons, 2008) and whether the perceived average simply reflects the sampling of a very small subset of the items in a set. In the present study, we explored this further in the context of faces, asking observers to judge the average expressions of sets of faces containing emotional outliers. Our results suggest that the visual system implicitly and unintentionally discounts the emotional outliers, thereby computing a summary representation that encompasses the vast majority of the information present. Additional computational modeling and behavioral results reveal that an intentional cognitive sampling strategy does not accurately capture observer performance. Observers derive precise ensemble information given a 250-msec exposure, suggesting a rapid and flexible system not bound by the limits of serial attention.

Summarizing Item Difficulty Variation with Parcel Scores

A new method for analyzing differential item functioning is proposed to investigate the relative strengths and weaknesses of multiple groups of examinees. Accordingly, the notion of a conditional measure of difference between two groups (Reference and Focal) is generalized to a conditional variance. The objective of this article is to present and illustrate a strategy for aggregating results across sets of similar items that exhibit item difficulty variation. Logically, this aggregation strategy is related to the idea of DIF amplification, but estimation is ultimately carried out in the framework of a confirmatory multidimensional Rasch model. Grade 4 data from the 2000 National Assessment of Educational Progress are used to illustrate the technique.