Showing papers on "Content-based image retrieval published in 2014"

Deep Learning for Content-Based Image Retrieval: A Comprehensive Study

Abstract: Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known ``semantic gap'' issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research.

A new matching strategy for content based image retrieval system

Abstract: Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.

Content-based image retrieval by integrating color and texture features

Abstract: Content-based image retrieval (CBIR) has been an active research topic in the last decade. Feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a content-based image retrieval method based on an efficient integration of color and texture features. As its color features, pseudo-Zernike chromaticity distribution moments in opponent chromaticity space are used. As its texture features, rotation-invariant and scale-invariant image descriptor in steerable pyramid domain are adopted, which offers an efficient and flexible approximation of early processing in the human visual system. The integration of color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for different test DBs.

A Survey on Content Based Image Retrieval Systems

Abstract: Various content-based image retrieval techniques are available for retrieving the require and classify images, we are reviewing them. In our first section, we are tending towards some basics of a particular CBIR system with that we have shown some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the next section, we have shown different distance measuring techniques used for similarity measurement of any image and also discussed indexing techniques. Finally conclusion and future scope is discussed.

Embedding neural networks for semantic association in content based image retrieval

Abstract: Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

Medical Image Retrieval: A Multimodal Approach.

Abstract: Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding

Abstract: Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top n similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to retrieve the similar facial images that truly match the query, and 2) how to exploit the noisy labels of the top similar facial images, which may be incorrect or incomplete due to the nature of web images. In this paper, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding by learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. An efficient optimization algorithm is proposed to solve the WLRLCC problem. Moreover, an effective sparse reconstruction scheme is developed to perform the face annotation task. We conduct extensive empirical studies on several web facial image databases to evaluate the proposed WLRLCC algorithm from different aspects. The experimental results validate its efficacy. We share the two constructed databases "WDB" (714,454 images of 6,025 people) and "ADB" (126,070 images of 1,200 people) with the public. To further improve the efficiency and scalability, we also propose an offline approximation scheme (AWLRLCC) which generally maintains comparable results but significantly reduces the annotation time.

Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation

Abstract: This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme.

Content Based Image Retrieval using Color Edge Detection and Discrete Wavelet Transform

Abstract: Color is one of the most important low-level features used in image retrieval and most content-based image retrievals (CBIR) systems use color as an image features. However, image retrieval using only color features often provide very unsatisfactory results because in many cases, images with similar colors do not have similar content. As the solution of this problem this paper describes a novel algorithm for Content Based Image Retrieval (CBIR) based on Color Edge Detection and Discrete Wavelet Transform (DWT). This method is different from the existing histogram based methods. The proposed algorithm generates feature vectors that combines both color and edge features. This paper also uses wavelet transform to reduce the size of the feature vector and simultaneously preserving the content details. The robustness of the system is also tested against query image alterations such as geometric deformations and noise addition etc. Wang's image database is used for experimental analysis and results are shown in terms of precision and recall.

Sketch-based image retrieval using keyshapes

Abstract: Although sketch based image retrieval (SBIR) is still a young research area, there are many applications capable of exploiting this retrieval paradigm, such as web searching and pattern detection. Moreover, nowadays drawing a simple sketch query turns very simple since touch screen based technology is being expanded. In this work, we propose a novel local approach for SBIR based on detecting simple shapes which are named keyshapes. Our method works as a local strategy, but instead of detecting keypoints, it detects keyshapes over which local descriptors are computed. Our proposal based on keyshapes allow us to represent the structure of the objects in an image which could be used to increase the effectiveness in the retrieval task. Indeed, our results show an improvement in the retrieval effectiveness with respect to the state of the art. Furthermore, we demonstrate that combining our keyshape approach with a Bag of Feature approach allows us to achieve significant improvement with respect to the effectiveness of the retrieval task.

Content Based Image Indexing and Retrieval.

Abstract: In this paper, we present the efficient content based image retrieval systems which employ the color, texture and shape information of images to facilitate the retrieval process. For efficient feature extraction, we extract the color, texture and shape feature of images automatically using edge detection which is widely used in signal processing and image compression. For facilitated the speedy retrieval we are implements the antipole-tree algorithm for indexing the images.

Content-based histopathology image retrieval using CometCloud.

Abstract: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Weighting scheme for image retrieval based on bag-of-visual-words

Abstract: Inspired by the success of bag-of-words in text retrieval, bag-of-visual-words and its variants are widely used in content-based image retrieval to describe visual content. Various weighting schemes have also been proposed to integrate different yet complementary visual-words. However, most of these weighting schemes tend to use fixed weight for every visual-word extracted from the query image, which may lose the discriminative information. This study presents a novel combining method which captures query-specific weights for visual-words in query image. The method mainly contains two stages. Firstly, in offline weight learning, the authors introduce a linear classifier to build a query-category mapping table, and max-margin learning to build category-weight mapping table. Query-category mapping table is used to map the query image to the most likely image class, and category-weight mapping table is used to map image class to the weights of visual-words. Secondly, in online weight mapping, the weights of visual-words are determined efficiently by looking into the pre-learned mapping tables. Experimental results on WANG database and Caltech 101 demonstrate that the proposed weighting scheme can effectively weight visual-words of query image according to their discriminative information. In addition, comparative experiments demonstrate the proposed weighting scheme can obtain higher retrieval performance than other weighting schemes.

An integrated approach to Content Based Image Retrieval

Abstract: Content based image retrieval, in the last few years has received a wide attention. Content Based Image Retrieval (CBIR) basically is a technique to perform retrieval of the images from a large database which are similar to image given as query. CBIR is closer to human semantics, in the context of image retrieval process. CBIR technique has its application in different domains such as crime prevention, medical images, weather forecasting, surveillance, historical research and remote sensing Here content refers to the visual information of images such as texture, shape and color. Contents of image are richer in information for an efficient retrieval in comparison to text based image retrieval. In this paper, we have proposed a content based image retrieval integrated technique which extracts both the color and texture feature. To extract the color feature, color moment (CM) is used on color images and to extract the texture feature, local binary pattern (LBP) is performed on the grayscale image. Then both color and texture feature of image are combined to form a single feature vector. In the end similarity matching is performed by Euclidian distance which compares feature vector of database images with query images. LBP mainly used for face recognition. But we are going to use LBP for natural images. This combined approach provides accurate, efficient, less complex retrieval system.

Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval

Abstract: Texture is one of the visual contents of an image used in content-based image retrieval (CBIR) to represent and index the image. Statistical textural representation methods characterize texture by the statistical distribution of the image intensity. This paper proposes a gray level statistical matrix from which four statistical texture features are estimated for the retrieval of mammograms from mammographic image analysis society (MIAS) database. The mammograms comprising architectural distortion, asymmetry, calcification, circumscribed, ill-defined, spiculated and normal classes are used in the experimentation. Precision, recall, retrieval rate, normalized average rank, average matching fraction, storage requirement and retrieval time are the performance measures used for the evaluation of retrieval performance. Using the proposed method, the highest mean precision rate obtained is 85.1 %. The results show that the proposed method outperforms the state-of-the-art texture feature extraction methods in mammogram retrieval problem.

Attribute-Augmented Semantic Hierarchy: Towards a Unified Framework for Content-Based Image Retrieval

Abstract: This article presents a novel attribute-augmented semantic hierarchy (A2SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in content-based image retrieval (CBIR). A2SH organizes semantic concepts into multiple semantic levels and augments each concept with a set of related attributes. The attributes are used to describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. An hierarchical semantic similarity function is learned to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedback on attributes and images. This feedback is then used to refine the search results based on A2SH. We use A2SH as a basis to develop a unified content-based image retrieval system. We conduct extensive experiments on a large-scale dataset of over one million Web images. Experimental results show that the proposed A2SH can characterize the semantic affinities among images accurately and can shape user search intent quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.

Content-Based Image Retrieval Using Moments of Local Ternary Pattern

Abstract: Due to the availability of large number of digital images, development of an efficient content-based indexing and retrieval method is required. Also, the emergence of smartphones and modern PDAs has further substantiated the need of such systems. This paper proposes a combination of Local Ternary Pattern (LTP) and moments for Content-Based Image Retrieval. Image is divided into blocks of equal size and LTP codes of each block are computed. Geometric moments of LTP codes of each block are computed followed by computation of distance between moments of LTP codes of query and database images. Then, the threshold using distance values is applied to retrieve images similar to the query image. Performance of the proposed method is compared with other state-of-the-art methods on the basis of results obtained on Corel-1,000 database. The comparison shows that the proposed method gives better results in terms of precision and recall as compared to other state-of-the-art image retrieval methods.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Abstract: This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

PinView: Implicit Feedback in Content-Based Image Retrieval

Abstract: This paper describes PinView, a content-based image retrieval system that exploits implicit relevance feedback collected during a search session. PinView contains several novel methods to infer the intent of the user. From relevance feedback, such as eye movements or pointer clicks, and visual features of images, PinView learns a similarity metric between images which depends on the current interests of the user. It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user. We have integrated PinView to the content-based image retrieval system PicSOM, which enables applying PinView to real-world image databases. With the new algorithms PinView outperforms the original PicSOM, and in online experiments with real users the combination of implicit and explicit feedback gives the best results.