The typical content-based image retrieval problem is to find images within a database that are similar to a given query image. This paper presents a solution to a different problem, namely that of content based sub-image retrieval, i.e., finding images from a database that contains another image. Note that this is different from finding a region in a (segmented) image that is similar to another image region given as a query. We present a technique for CBsIR that explores relevance feedback, i.e., the user's input on intermediary results, in order to improve retrieval efficiency. Upon modeling images as a set of overlapping and recursive tiles, we use a tile re-weighting scheme that assigns penalties to each tile of the database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant and irrelevant images identified by the user. Each tile is modeled by means of its color content using a compact but very efficient method which can, indirectly, capture some notion of texture as well, despite the fact that only color information is maintained. Performance evaluation on a largely heterogeneous dataset of over 10,000 images shows that the system can achieve a stable average recall value of 70 within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds on an off-the-shelf desktop computer.
The BIC approach was shown in @cite_0 to outperform several other CBIR approaches and, as such, we adopt it in our CBsIR proposal to extract and compare the visual feature of each tile with the goal of improving the retrieval accuracy.
@cite_0
2039388884
This paper presents (Border Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L 1 ): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50 the space requirement to represent a histogram. This paper proposes a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach. Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high-level concepts and low-level features, and (2) the subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely. Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice. Local descriptors are mostly extracted over the raw intensity values of the image and requires the analysis of patterns in local pixel neighborhoods. The raw images don't provide much local relationship information required for the local descriptors. To resolve this problem, a new method of local image feature description is proposed in this paper based on the patterns extracted from the multiple filtered images. The whole process is structured as follows: First, the images are filtered with the bag of filters (BoF) and then local binary pattern (LBP) is computed over each filtered image and finally concatenated to find out the single BoF-LBP descriptor. The local information enriches in the filtered images improves the discriminative ability of the final descriptor. Content based image retrieval (CBIR) experiments are performed to observe the effectiveness of the proposed approach and compared with the recent state-of-the-art methods. The experiments are conducted over four benchmark databases having natural and texture images such as Corel-1k, Corel-10k, MIT-VisTex and STex-512S. The experimental results confirm that the introduced approach is able to improve the retrieval performance of the CBIR system. In Content-based Image Retrieval (CBIR) systems, ranking accurately collection images is of great relevance. Users are interested in the returned images placed at the first positions, which usually are the most relevant ones. Collection images are ranked in increasing order of their distance to the query pattern (e.g., query image) defined by users. Therefore, the effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel context-based approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. In our approach, distances among images are redefined based on the similarity of their ranked lists. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method. Automatic clothes search in consumer photos is not a trivial problem as photos are usually taken under completely uncontrolled realistic imaging conditions. In this paper, a novel framework is presented to tackle this issue by leveraging low-level features (e.g., color) and high-level features (attributes) of clothes. First, a content-based image retrieval(CBIR) approach based on the bag-of-visual-words (BOW) model is developed as our baseline system, in which a codebook is constructed from extracted dominant color patches. A reranking approach is then proposed to improve search quality by exploiting clothes attributes, including the type of clothes, sleeves, patterns, etc. The experiments on photo collections show that our approach is robust to large variations of images taken in unconstrained environment, and the reranking algorithm based on attribute learning significantly improves retrieval performance in combination with the proposed baseline. The last decade witnessed a growing interest in research on content-based image retrieval (CBIR) and related areas. Several systems for managing and retrieving images have been proposed, each one tailored to a specific application. Functionalities commonly available in CBIR systems include: storage and management of complex data, development of feature extractors to support similarity queries, development of index structures to speed up image retrieval, and design and implementation of an intuitive graphical user interface tailored to each application. To facilitate the development of new CBIR systems, we propose an image-handling extension to the relational database management system (RDBMS) PostgreSQL. This extension, called PostgreSQL-IE, is independent of the application and provides the advantage of being open source and portable. The proposed system extends the functionalities of the structured query language SQL with new functions that are able to create new feature extraction procedures, new feature vectors as combinations of previously defined features, and new access methods, as well as to compose similarity queries. PostgreSQL-IE makes available a new image data type, which permits the association of various images with a given unique image attribute. This resource makes it possible to combine visual features of different images in the same feature vector. To validate the concepts and resources available in the proposed extended RDBMS, we propose a CBIR system applied to the analysis of mammograms using PostgreSQL-IE. Given the growing amount of very large image databases, content based image retrieval (CBIR) is becoming more and more important. One of the major challenges in CBIR is the semantic gap - commonly used feature-based algorithms are not able to identify what really draws human attention in an image. This problem is more crucial for localized CBIR, where certain regions parts of the image are what the user is really interested in. This paper explores how human gaze can be utilized to extract regions of interest (ROIs) of an image to perform attention based image retrieval. Using eye tracking data and knowledge about foveal and peripheral vision of humans, we present a foveal fixation clustering algorithm that automatically generates ROIs in an image while a person is viewing it. To objectively set different parameters of the algorithm, a small user study was conducted. The method was evaluated for use in a localized CBIR system. Image retrieval results using the publicly available SIVAL dataset were scored using mean average precision (MAP). Comparison to a saliency-based visual attention algorithm as well as to manually labeled regions showed that the retrieval results of the developed algorithm are nearly two times better than the saliency-based visual attention algorithm and very close to the results using hand-labeled regions. Relevance feedback (RF) mechanisms are widely adopted in Content-Based Image Retrieval (CBIR) systems to improve image retrieval performance. However, there exist some intrinsic problems: (1) the semantic gap between high-level concepts and low-level features and (2) the subjectivity of human perception of visual contents. The primary focus of this paper is to evaluate the possibility of inferring the relevance of images based on eye movement data. In total, 882 images from 101 categories are viewed by 10 subjects to test the usefulness of implicit RF, where the relevance of each image is known beforehand. A set of measures based on fixations are thoroughly evaluated which include fixation duration, fixation count, and the number of revisits. Finally, the paper proposes a decision tree to predict the user's input during the image searching tasks. The prediction precision of the decision tree is over 87 , which spreads light on a promising integration of natural eye movement into CBIR systems in the future. This paper deals with the problem of finding images that contain a given query image, the so-called content-based sub-image retrieval. We propose an approach based on a hierarchical tree that encodes the color feature of image tiles which are in turn stored as an index sequence. The index sequences of both candidate images and the query sub-image are then compared in order to rank the database images suitability with respect to the query. In our experiments, using 10,000 images and disk-resident metadata, for 60Σ (80Σ) of the queries the relevant image, i.e., the one where the query sub-image was extracted from, was found among the first 10 (50) retrieved images in about 0.15 sec. With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and Web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery. Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time. Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy. Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52 0.81, respectively for the test set. In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0 of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features. Our previous shape-based 3D model retrieval algorithm compares 3D shapes by using thousands of local visual features per model. A 3D model is rendered into a set of depth images, and from each image, local visual features are extracted by using the Scale Invariant Feature Transform (SIFT) algorithm by Lowe. To efficiently compare among large sets of local features, the algorithm employs bag-of-features approach to integrate the local features into a feature vector per model. The algorithm outperformed other methods for a dataset containing highly articulated yet geometrically simple 3D models. For a dataset containing diverse and detailed models, the method did only as well as other methods. This paper proposes an improved algorithm that performs equal or better than our previous method for both articulated and rigid but geometrically detailed models. The proposed algorithm extracts much larger number of local visual features by sampling each depth image densely and randomly. To contain computational cost, the method utilizes GPU for SIFT feature extraction and an efficient randomized decision tree for encoding SIFT features into visual words. Empirical evaluation showed that the proposed method is very fast, yet significantly outperforms our previous method for rigid and geometrically detailed models. For the simple yet articulated models, the performance was virtually unchanged. Multimodal Retrieval efficiency can be improved by textual query reformulation.A graph based keyphrase extraction incorporating correlation of terms is proposed.Textual query is expanded with relevant part of narratives and extracted keyphrases.Text and image features are combined using a weightlearning model.The proposed method improves both image and text retrieval efficiency significantly. Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval. Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ?semantic gap? problem. In this paper, an innovative approach is proposed that combines a relevance feedback (RF) approach with an evolutionary stochastic algorithm, called particle swarm optimizer (PSO), as a way to grasp user's semantics through optimized iterative learning. The retrieval uses human interaction to achieve a twofold goal: 1) to guide the swarm particles in the exploration of the solution space towards the cluster of relevant images; 2) to dynamically modify the feature space by appropriately weighting the descriptive features according to the users' perception of relevance. Extensive simulations showed that the proposed technique outperforms traditional deterministic RF approaches of the same class, thanks to its stochastic nature, which allows a better exploration of complex, nonlinear, and highly-dimensional solution spaces. A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highly desired to combine multiple types of different feature representations and diverse distance measures in order to close the semantic gap. In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of different retrieval schemes by learning from large-scale training data in CBIR. We first formulate the problem formally as a learning to rank task, which can be solved in general by applying the existing batch learning to rank algorithms from text information retrieval (IR). To further address the scalability towards large-scale online CBIR applications, we present a family of online learning to rank algorithms, which are significantly more efficient and scalable than classical batch algorithms for large-scale online CBIR. Finally, we conduct an extensive set of experiments, in which encouraging results show that our technique is effective, scalable and promising for large-scale CBIR.