Image content-based retrieval using chromaticity moments

A number of different approaches have been recently presented for image retrieval using color features. Most of these methods use the color histogram or some variation of it. If the extracted information is to be stored for each image, such methods may require a significant amount of space for storing the histogram, depending on a given image's size and content. In the method proposed, only a small number of features, called chromaticity moments, are required to capture the spectral content (chrominance) of an image. The proposed method is based on the concept of the chromaticity diagram and extracts a set of two-dimensional moments from it to characterize the shape and distribution of chromaticities of the given image. This representation is compact (only a few chromaticity moments per image are required) and constant (independent of image size and content), while its retrieval effectiveness is comparable to using the full chromaticity histogram.     

Association and content-based retrieval

In spite of important efforts in content-based indexing and retrieval during these last years, seeking relevant and accurate images remains a very difficult query. In the state-of-the-art approaches, the retrieval task may be efficient for some queries in which the semantic content of the query can be easily translated into visual features. For example, finding images of fires is simple because fires are characterized by specific colors (yellow and red). However, it is not efficient in other application fields in which the semantic content of the query is not easily translated into visual features. For example, finding images of birds during migrations is not easy because the system has to understand the query semantic. In the query, the basic visual features may be useful (a bird is characterized by a texture and a color), but they are not sufficient. What is missing is the generalization capability. Birds during migrations belong to the same repository of birds, so they share common associations among basic features (e.g., textures and colors) that the user cannot specify explicitly. We present an approach that discovers hidden associations among features during image indexing. These associations discriminate image repositories. The best associations are selected on the basis of measures of confidence. To reduce the combinatory explosion of associations, because images of the database contain very large numbers of colors and textures, we consider a visual dictionary that group together similar colors and textures.     

Localized content-based image retrieval

We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble

With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.     

Securing content-based image retrieval on the cloud using generative models

Multimedia Tools and Applications - Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image...     

Probability based document clustering and image clustering using content-based image retrieval

Clustering of related or similar objects has long been regarded as a potentially useful contribution of helping users to navigate an information space such as a document collection. Many clustering algorithms and techniques have been developed and implemented but as the sizes of document collections have grown these techniques have not been scaled to large collections because of their computational overhead. To solve this problem, the proposed system concentrates on an interactive text clustering methodology, probability based topic oriented and semi-supervised document clustering. Recently, as web and various documents contain both text and large number of images, the proposed system concentrates on content-based image retrieval (CBIR) for image clustering to give additional effect to the document clustering approach. It suggests two kinds of indexing keys, major colour sets (MCS) and distribution block signature (DBS) to prune away the irrelevant images to given query image. Major colour sets are related with colour information while distribution block signatures are related with spatial information. After successively applying these filters to a large database, only small amount of high potential candidates that are somewhat similar to that of query image are identified. Then, the system uses quad modelling method (QM) to set the initial weight of two-dimensional cells in query image according to each major colour and retrieve more similar images through similarity association function associated with the weights. The proposed system evaluates the system efficiency by implementing and testing the clustering results with Dbscan and K-means clustering algorithms. Experiment shows that the proposed document clustering algorithm performs with an average efficiency of 94.4% for various document categories.     

Complementary relevance feedback-based content-based image retrieval

We propose a complementary relevance feedback-based content-based image retrieval (CBIR) system. This system exploits the synergism between short-term and long-term learning techniques to improve the retrieval performance. Specifically, we construct an adaptive semantic repository in long-term learning to store retrieval patterns of historical query sessions. We then extract high-level semantic features from the semantic repository and seamlessly integrate low-level visual features and high-level semantic features in short-term learning to effectively represent the query in a single retrieval session. The high-level semantic features are dynamically updated based on users’ query concept and therefore represent the image’s semantic concept more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space on a large scale imagery database.     

From content-based retrieval to invisible telepresence

Better tools for producing and managing data, combined with the human desire for information, have resulted in a data explosion. Indeed, data overload often leaves us confused, disoriented, and inefficient. The challenge is to find relevant data that lets us extract the information we want. Keyword-based systems cannot do this well, especially when working with images and video. It will be impossible to cope with the multimedia data explosion unless the data is organized for rapid information retrieval. Research in this field is in its infancy; nevertheless, commercial products are starting to appear that allow retrieval of images and video using query by pictorial example techniques. At present, these techniques work using only image primitives, but similar techniques based on domain knowledge should be available soon. Thus we will have additional techniques for providing navigational engines to ensure that the information highway is more than just a data network     

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

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

Multimedia Tools and Applications - 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...     

MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features

Multimedia Tools and Applications - Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is an open...     

Exploiting concept clusters for content-based information retrieval

Current approaches to index weighting for information retrieval from texts are based on statistical analysis of the texts' contents. A key shortcoming of these indexing schemes, which consider only the terms in a document, is that they cannot extract semantically exact indexes that represent the semantic content of a document. To address this issue, we proposed a new indexing formalism that considers not only the terms in a document, but also the concepts. In the proposed method, concepts are extracted by exploiting clusters of terms that are semantically related, referred to as concept clusters. Through experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms an indexing method based on term frequency (TF), especially in regard to the highest-ranked documents. Moreover, the index term dimension was 53.3% lower for the proposed method than for the TF-based method, which is expected to significantly reduce the document search time in a real environment.     

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval (CBIR) systems that employs multiple visual features to characterize image content. Most previous feature aggregation schemes apply parallel topology, e.g., the linear combination scheme, which suffer from two problems. First, the function of individual visual feature is limited since the ranks of the retrieved images are determined only by the combined similarity. Second, the irrelevant images seriously affect the retrieval performance of feature aggregation scheme since all images in a collection will be ranked. To address these problems, we propose a new feature aggregation scheme, series feature aggregation (SFA). SFA selects relevant images using visual features one by one in series from the images highly ranked by the previous visual feature. The irrelevant images will be effectively filtered out by individual visual features in each stage, and the remaining images are collectively described by all visual features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that the proposed SFA can outperform conventional parallel feature aggregation schemes.     

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.     

A Center-Surround Histogram for content-based image retrieval

In this paper, a new type of histogram which incorporates only the visual information surrounding the edges of the image is introduced. The edge extraction operation is performed with the use of a center-surround operator of the Human Visual System. The proposed Center-Surround Histogram (CSH) has two main advantages over the classic histogram. First, it reduces the amount of visual information that needs to be processed and second, it incorporates a degree of spatial information when used in content based image retrieval applications. The method is compared with other contemporary image retrieval methods, including that of another edge color histogram, on two different databases. The comparison shows that the use of CSH exhibits better results in shorter execution times.     

Visual information retrieval system via content-based approach

In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.     

Cluster-driven refinement for content-based digital image retrieval

Increasing application demands are pushing databases toward providing effective and efficient support for content-based retrieval over multimedia objects. In addition to adequate retrieval techniques, it is also important to enable some form of adaptation to users' specific needs. This paper introduces a new refinement method for retrieval based on the learning of the users' specific preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, with each cluster represented by a prototype. Clustering constructs a taxonomy of objects by forming groups of closely-related objects. The proposed approach to learn the users' preferences is to refine corresponding clusters from objects provided by the users in the foreground, and to simultaneously adapt the database index in the background. Queries can be performed based solely on shape, or on a combination of shape with other features such as color. Our experimental results show that the system successfully adapts queries into databases with only a small amount of feedback from the users. The quality of the returned results is superior to that of a color-based query, and continues to improve with further use.