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When virtualizing large-scale images of the real world, online hashing provides an efficient scheme for fast retrieval and compact storage. It converts high-dimensional streaming data into compact binary hash codes while saving the structural characteristics between samples into the Hamming space. Existing works usually update the hashing function based on the similarity between input data, or design a codebook to assign code words for each single input sample. However, assigning code words to multiple samples while retaining the balanced similarity of the image instances is still challenging. To address this issue, we propose a novel discriminative similarity-balanced online hashing (DSBOH) framework in this work. In particular, we first obtain the Hadamard codebook that guides the generation of discriminative binary codes according to label information. Then, we maintain the correlation between the new data and the previously arrived data by the balanced similarity matrix, which is also generated by semantic information. Finally, we joined the Hadamard codebook and the balanced similarity matrix into a unified hashing function to simultaneously maintain discrimination and balanced similarity. The proposed method is optimized by an alternating optimization technique. Extensive experiments on the CIFAR-10, MNIST, and Places205 datasets demonstrate that our proposed DSBOH performs better than several state-of-the-art online hashing methods in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

As an effective and efficient way to graphically portray ideas and concepts, free-hand sketches are playing an increasingly significant role in today's image retrieval systems. There are many factors that contribute to a successful sketch-based image retrieval (SBIR) system, but perhaps the most essential ones are the design of powerful image feature descriptors and effective perceptual similarity metrics in the feature space. However, the painting experiences and styles of users vary hugely, which poses a great challenge for consistently accurate feature representation and matching. We introduce an approach for SBIR by characterizing homogeneous sketch drawing styles. Specifically, given a sketch query, the proposed method first infers the user's painting style from it by analyzing contour features. Then, top K-nearest sketches of historical users with similar drawing characters are identified from the database. Finally, the matched images of the query can be obtained by reranking those of the K-nearest sketches stored in the historical recordings. To evaluate the effectiveness of the proposed method, we conducted quantitative comparisons with several peer SBIR methods. Experimental results indicate that the proposed method is 15% higher than state-of-the-art in terms of mean average precision. [ABSTRACT FROM AUTHOR]

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine‐grained image ranking. We propose to combine attribute‐based representation and online passive‐aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine‐grained image ranking, we introduce a supervised constrained clustering method to gather class‐balanced training instances for local PA‐based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state‐of‐the‐art methods in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

Texture characteristic is an important attribute of medical images, and has been applied in many medical image applications. This paper proposes a simple approach to employ the texture features of medical images for retrieval. The developed approach first conducts image filtering to medical images using different Gabor and Schmid filters, and then uniformly partitions the filtered images into non-overlapping patches. These operations provide extensive local texture information of medical images. The bag-of-words model is finally used to obtain feature representations of the images. Compared with several existing features, the proposed one is more discriminative and efficient. Experiments on two benchmark medical CT image databases have demonstrated the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Motivation: Diagrams embedded in the biomedical literature convey rich contents, which often concisely and intuitively highlight key thesis of a research article. Despite their vital importance and informative clues for biomedical literature navigation and retrieval; currently, we miss an effective computational method for automatically understanding and accessing these valuable resources.Proposed Method: To address the aforementioned gap, we propose a novel context-based algorithm for estimating the similarity between a pair of biomedical diagrams. The main difference of the proposed algorithm with respect to the existing methods lies in the new algorithm’s incorporation of the semantic context associated with diagrams in their source documents into the diagram similarity estimation process. In addition, the new approach also performs a series of advanced image processing and text mining operations to comprehensively extract the semantic content graphically encoded inside diagram images.Results: The new algorithm can be deployed as a reusable component providing a fundamental function for building many advanced, semantic-aware applications on biomedical diagram processing. As a case study, in our experiments, we demonstrate the advantage of the new algorithm for diagram retrieval. A set of biomedical diagram search and ranking experiments were conducted, where the performance of the new method was compared with that of five peer methods. The comparison results demonstrate the performance superiority of the new algorithm with all peer methods with statistical significance.Contact: songhua.xu@njit.edu, shengjianqiang@163.com or lnslxn@mail.sysu.edu.cn. [ABSTRACT FROM AUTHOR]

Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling. Learning pairwise image similarity is of great importance to an image retrieval task. With the resurgence of Convolutional Neural Networks (CNNs), recent works have achieved significant progresses via deep representation learning with metric embedding, which drives similar examples close to each other in a feature space, and dissimilar ones apart from each other. In this paper, we propose a novel embedding method termed focus ranking that can be easily unified into a CNN for jointly learning image representations and metrics in the context of fine-grained fabric image retrieval. Focus ranking aims to rank similar examples higher than all dissimilar ones by penalizing ranking disorders via the minimization of the overall cost attributed to similar samples being ranked below dissimilar ones. At the training stage, training samples are organized into focus ranking units for efficient optimization. We build a large-scale fabric image retrieval dataset (FIRD) with about 25,000 images of 4300 fabrics, and test the proposed model on the FIRD dataset. Experimental results show the superiority of the proposed model over existing metric embedding models. [ABSTRACT FROM AUTHOR]

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes. [ABSTRACT FROM AUTHOR]

Feature fusion in medical image retrieval remains a challenging task because of the high-dimensional data and massive amount of irrelevant information in images. To solve these issues, we propose a novel feature fusion method, called fine-grained correlation analysis (FGCA), for medical image retrieval. First, we analyze the problem that there are many irrelevant local regions in a category. To solve this problem, an image is partitioned into some fine-grained samples. Then, the fine-grained samples with similar characteristics are tagged with the same label by the k-means clustering algorithm. Finally, we investigate how the correlation relationship extracted from the fine-grained samples helps fuse different features and obtain the more discriminative and less redundant information for medical image retrieval. Experiments on three medical image datasets show that our proposed FGCA approach works better than the conventional methods. [Display omitted] • Propose a fine-grained correlation analysis (FGCA) algorithm. • FGCA can fuse different features based on fine-grained samples. • FGCA can find discriminative features with low dimensions. • FGCA can improve performance for medical images retrieval. [ABSTRACT FROM AUTHOR]