Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Relevance- and interface-driven clustering for visual information retrieval

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.     

基于局部特征的模具数控加工推荐系统

为了实现对已有数控工艺设计成果的有效重用，提出了一种基于局部特征匹配的数控工艺推荐系统模型，该模型利用局部特征级的三维极半径矩结合几何尺寸等其他几何语义信息建立加工几何特征，并对典型局部特征对应的数控工艺信息进行有效组织与表达，构建数控工艺案例库。设计了一种基于综合加权的加工几何特征的相似性度量方法，以此实现局部特征的检索与加工特征的准确匹配，并根据匹配程度定义数控工艺实例复用方法，结合用户加工需求，最终产生合适的数控工艺推荐方案。     

Optimal depth estimation using modified Kalman filter in the presence of non‐Gaussian jitter noise

The consideration of the noise that affects 3D shape recovery is becoming very important for accurate shape reconstruction. In Shape from Focus, when 2D image sequences are obtained, mechanical vibrations, referred as jitter noise, occur randomly along the z‐axis, in each step. To model the noise for real world scenarios, this article uses Lévy distribution for noise profile modeling. Next, focus curves acquired by one of focus measure operators are modeled as Gaussian function to consider the effects of the jitter noise. Finally, since conventional Kalman filter provides good output under Gaussian noise only, a modified Kalman filter, as proposed method, is used to remove the jitter noise. Experiments are carried out using synthetic and real objects to show the effectiveness of the proposed method.     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

基于跨语言词向量模型的蒙汉查询词扩展方法研究

跨语言信息检索指以一种语言为检索词,检索出用另一种或几种语言描述的一种信息的检索技术,是信息检索领域重要的研究方向之一。近年来,跨语言词向量为跨语言信息检索提供了良好的词向量表示,受到很多学者的关注。该文首先利用跨语言词向量模型实现汉文查询词到蒙古文查询词的映射,其次提出串联式查询扩展、串联式查询扩展过滤、交叉验证筛选过滤三种查询扩展方法对候选蒙古文查询词进行筛选和排序,最后选取上下文相关的蒙古文查询词。实验结果表明: 在蒙汉跨语言信息检索任务中引入交叉验证筛选方法对信息检索结果有很大的提升。     

Building a text collection for Urdu information retrieval

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.