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A novel scheme with deep cross-modal correlation learning is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR) for large-scale annotated images. It integrates the deep multimodal feature generation, deep cross-modal correlation learning and similarity search optimization through mining all the beneficial multimodal information sources in sketches and images,...
Neural word vector (NWV) such as word2vec is a powerful text representation tool that can encode extensive semantic information into compact vectors. This ability poses an interesting question in relation to image processing research - Can we learn better semantic image features from NWVs? We empirically explore this question in the context of semantic content-based image retrieval (CBIR). In this...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
Image annotation is a fundamental and challenging task in the field of semantic image retrieval. In this paper, we deal with image annotation via matrix completion. Concretely, we formulate the problem of annotating the tags of an image into a constrained optimization problem, in which the constraint is to keep the consistency with the given initial labels and the objective is to minimize the discrepancy...
We revisit text-based image retrieval for social media, exploring the opportunities offered by statistical semantics. We assess the performance and limitation of several complementary corpus-based semantic text similarity methods in combination with word representations. We compare results with state-of-the-art text search engines. Our deep learning-based semantic retrieval methods show a statistically...
In this work, we propose a Latent Semantic Association Retrieval(LSAR) method to break the bottleneck of the low-level feature based medical image retrieval. The method constructs the high-level semantic correlations among patients based on the low-level feature set extracted from the images. Specifically, a Pair-LDA model is firstly designed to refine the topic generation process of traditional Latent...
It is interesting and challenging to learn underlying semantics from multimodal data of different modalities, which carry their own contribution to high-level semantics. However, multimodal data are usually represented with heterogeneous features. It is difficult to learn a semantic subspace where multimodal correlation is learned and preserved. In this paper, we analyze sparse canonical correlation...
The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, however the semantic gap between low-level image features and high-level semantic concepts handled by the user, is one of the main problems in the image retrieval. On the other hand, the relevance feedback has been used on many CBIR systems such as an effective solution...
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Different statistical models and machine learning methods have been introduced for image auto-annotation. In this paper, we propose a collaborative approach, in which multiple different statistical models are combined effectively to predict the annotation for each image. Moreover, we combine both...
Automatic image tagging (AIT) is an effective technology to facilitate the process of image retrieval without requiring user to provide a retrieval instance beforehand. In this paper, we propose an AIT method based on kernel canonical correlation analysis (KCCA) with similarity refinement (KCCSR). As a statistic correlation technique, the KCCA aims at extracting some kind of hidden information shared...
Image retrieval is one of the hottest fields of computer vision and pattern recognition. In recent years, many researchers addressed the challenging problem of interpreting the semantics of images. This paper presented a novel approach based on relation net (concept and semantic keyword relation net) for high level semantic retrieval of Thangka image. Here, we use Delphi method and fuzzy statistic...
The amount of multimedia data on personal devices and the Web is increasing daily. Image browsing and retrieval systems in a low-dimensional space have been widely studied to manage and view large numbers of images. It is essential for such systems to exploit an efficient similarity measure of the images when searching for them. Existing methods use the distance in a low-level image feature space...
A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with...
at the present time, the increase of e-mail spam are heavy to cumber and the spam are vastly spread. These spams cause various problems to the Internet users, such as full incoming mailbox, and wasting time. Therefore, tremendous methods have been proposed but most of them have limitation in the mapping feature and processing time. This paper proposed a method that can detect a set of image e-mail...
A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a...
Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities...
Does there exist a compact set of visual topics in form of keyword clusters capable to represent all images visual content within an acceptable error? In this paper, we answer this question by analyzing distribution laws for keywords from image descriptions and comparing with traditional techniques in NLP, thereby propose three assumptions: Sparse Distribution Attribute, Local Convergent Assumption...
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