A powerful relevance feedback mechanism for content-based 3D model retrieval

The technique of relevance feedback has been introduced to content-based 3D model retrieval, however, two essential issues which affect the retrieval performance have not been addressed. In this paper, a novel relevance feedback mechanism is presented, which effectively makes use of strengths of different feature vectors and perfectly solves the problem of small sample and asymmetry. During the retrieval process, the proposed method takes the user’s feedback details as the relevant information of query model, and then dynamically updates two important parameters of each feature vector, narrowing the gap between high-level semantic knowledge and low-level object representation. The experiments, based on the publicly available 3D model database Princeton Shape Benchmark (PSB), show that the proposed approach not only precisely captures the user’s semantic knowledge, but also significantly improves the retrieval performance of 3D model retrieval. Compared with three state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval effectiveness only with a few rounds of relevance feedback based on several standard measures.

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

Mixture of KL subspaces for relevance feedback

Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.

Mental image search by boolean composition of region categories

Existing content-based image retrieval paradigms almost never address the problem of starting the search, when the user has no starting example image but rather a mental image. We propose a new image retrieval system to allow the user to perform mental image search by formulating boolean composition of region categories. The query interface is a region photometric thesaurus which can be viewed as a visual summary of salient regions available in the database. It is generated from the unsupervised clustering of regions with similar visual content into categories. In this thesaurus, the user simply selects the types of regions which should and should not be present in the mental image (boolean composition). The natural use of inverted tables on the region category labels enables powerful boolean search and very fast retrieval in large image databases. The process of query and search of images relates to that of documents with Google. The indexing scheme is fully unsupervised and the query mode requires minimal user interaction (no example image to provide, no sketch to draw). We demonstrate the feasibility of such a framework to reach the user mental target image with two applications: a photo-agency scenario on Corel Photostock and a TV news scenario. Perspectives will be proposed for this simple and innovative framework, which should motivate further development in various research areas.

Confidence interval approach to feature re-weighting

Relevance feedback is commonly incorporated into content-based image retrieval systems with the objective of improving retrieval accuracy via user feedback. One effective method for improving retrieval performance is to perform feature re-weighting based on the obtained feedback. Previous approaches to feature re-weighting via relevance feedback assume the feature data for images can be represented in fixed-length vectors. However, many approaches are invalidated with the recent development of features that cannot be represented in fixed-length vectors. In addition, previous approaches use only the information from the set of images returned in the latest query result for feature re-weighting. In this paper, we propose a feature re-weighting approach that places no restriction on the representation of feature data and utilizes the aggregate set of images returned over the iterations of retrieval to obtain feature re-weighting information. The approach analyzes the feature distances calculated between the query image and the resulting set of images to approximate the feature distances for the entire set of images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how the feature representations are structured. The experimental results show the effectiveness of the proposed approach and in comparisons with other work, it is shown that our approach outperforms previous work.

<Emphasis Type="Italic">I-Quest</Emphasis>: an <Emphasis Type="Italic">i</Emphasis>ntelligent <Emphasis Type="Italic">que</Emphasis>ry <Emphasis Type="Italic">st</Emphasis>ructuring based on user browsing feedback for semantic retrieval of video data

In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting sets is impossible or yields poor results.

OCRS: an interactive object-based image clustering and retrieval system

In this paper, we propose an Interactive Object-based Image Clustering and Retrieval System (OCRS). The system incorporates two major modules: Preprocessing and Object-based Image Retrieval. In preprocessing, an unsupervised segmentation method called WavSeg is used to segment images into meaningful semantic regions (image objects). This is an area where a huge number of image regions are involved. Therefore, we propose a Genetic Algorithm based algorithm to cluster these images objects and thus reduce the search space for object-based image retrieval. In the learning and retrieval module, the Diverse Density algorithm is adopted to analyze the user’s interest and generate the initial hypothesis which provides a prototype for future learning and retrieval. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. In interacting with user, we propose to use One-Class Support Vector Machine (SVM) to learn the user’s interest and refine the returned result. Performance is evaluated on a large image database and the effectiveness of our retrieval algorithm is demonstrated through comparative studies.

Object discovery in high-resolution remote sensing images: a semantic perspective

Given its importance, the problem of object discovery in high-resolution remote-sensing (HRRS) imagery has received a lot of attention in the literature. Despite the vast amount of expert endeavor spent on this problem, more efforts have been expected to discover and utilize hidden semantics of images for object detection. To that end, in this paper, we address this problem from two semantic perspectives. First, we propose a semantic-aware two-stage image segmentation approach, which preserves the semantics of real-world objects during the segmentation process. Second, to better capture semantic features for object discovery, we exploit a hyperclique pattern discovery method to find complex objects that consist of several co-existing individual objects that usually form a unique semantic concept. We consider the identified groups of co-existing objects as new feature sets and feed them into the learning model for better performance of image retrieval. Experiments with real-world datasets show that, with reliable segmentation and new semantic features as starting points, we can improve the performance of object discovery in terms of various external criteria.

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Semantic representation of multimedia content: Knowledge representation and semantic indexing

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.

Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval

Query refinement and feature re-weighting are the two core techniques underlying the relevance feedback of content-based image retrieval. Most existing relevance feedback mechanisms generally model the user’s query target with a single query point and weight each extracted feature with a single importance factor. A designed estimation procedure then estimates the best query point and all importance factors by optimizing a formulated criterion which measures the goodness of the estimation. This formulated criterion simultaneously encapsulates all positive and negative examples supplied from the user’s feedback. Under such formulation, the positive and negative examples may contribute contradictorily to the criterion and sometimes may introduce higher difficulty in attaining a good estimation. In this paper, we propose a different statistical formulation to estimate independently two pairs of query points and feature weights from the positive examples and negative examples, respectively. These two pairs then define the likelihood ratio, a criterion term used to rank the relevance of all database images. This approach simplifies the criterion formulation and also avoids the mutual impeditive influence between positive examples and negative examples. The experimental results demonstrate that the proposed approach outperforms some other related approaches.

Efficient kNN search in polyphonic music databases using a lower bounding mechanism

Querying polyphonic music from a large data collection is an interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.     

Video scene segmentation and semantic representation using a novel scheme

Grouping video content into semantic segments and classifying semantic scenes into different types are the crucial processes to content-based video organization, management and retrieval. In this paper, a novel approach to automatically segment scenes and semantically represent scenes is proposed. Firstly, video shots are detected using a rough-to-fine algorithm. Secondly, key-frames within each shot are selected adaptively with hybrid features, and redundant key-frames are removed by template matching. Thirdly, spatio-temporal coherent shots are clustered into the same scene based on the temporal constraint of video content and visual similarity between shot activities. Finally, under the full analysis of typical characters on continuously recorded videos, scene content is semantically represented to satisfy human demand on video retrieval. The proposed algorithm has been performed on various genres of films and TV program. Promising experimental results show that the proposed method makes sense to efficient retrieval of interesting video content.

SVM-based Interactive Document Retrieval with Active Learning

This paper describes an application of SVM (Support Vector Machines) to interactive document retrieval using active learning. Some works have been done to apply classification learning like SVM to relevance feedback and have obtained successful results. However they did not fully utilize characteristic of example distribution in document retrieval. We propose heuristics to bias document showing for user’s judgement according to distribution of examples in document retrieval. This heuristics is executed by selecting examples to show a user in neighbors of positive support vectors, and it improves learning efficiency. We implemented a SVM-based interactive document retrieval system using our proposed heuristics, and compared it with conventional systems like Rocchio-based system and a SVM-based system without the heuristics. We conducted systematic experiments using large data sets including over 500,000 newspaper articles and confirmed our system outperformed other ones.

An index and retrieval framework integrating perceptive features and semantics for multimedia databases

Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this paper, we propose a novel framework for multimedia databases index and retrieval integrating the perceptive features and semantic classes to improve the speed and the precision of the content-based multimedia retrieval (CBMR). We develop a semantics supervised clustering based index approach (briefly as SSCI): the entire data set is divided hierarchically into many clusters until the objects within a cluster are not only close in the perceptive feature space but also within the same semantic class, and then an index term is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two phases: first, the indexes of all clusters are scanned sequentially to get the candidate clusters with the smallest distances from the query example; second, the original feature vectors within the candidate clusters are visited to get search results. Furthermore, if the results are not satisfied, the SSCI supports an effective relevance feedback (RF) search: users mark the positive and negative samples regarded a cluster as unit instead of a single object; then the Bayesian classifiers on perceptive features and that on semantics are used respectively to adjust retrieval similarity distance. Our experiments show that SSCI-based searching was faster than VA⁺-based searching; the quality of the search result based on SSCI was better than that of the sequential search in terms of semantics; and a few cycles of the RF by the proposed approach can improve the retrieval precision significantly.

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

Harmonizing Hierarchical Manifolds for Multimedia Document Semantics Understanding and Cross-Media Retrieval

In this paper, we consider the problem of multimedia document (MMD) semantics understanding and content-based cross-media retrieval. An MMD is a set of media objects of different modalities but carrying the same semantics and the content-based cross-media retrieval is a new kind of retrieval method by which the query examples and search results can be of different modalities. Two levels of manifolds are learned to explore the relationships among all the data in the level of MMD and in the level of media object respectively. We first construct a Laplacian media object space for media object representation of each modality and an MMD semantic graph to learn the MMD semantic correlations. The characteristics of media objects propagate along the MMD semantic graph and an MMD semantic space is constructed to perform cross-media retrieval. Different methods are proposed to utilize relevance feedback and experiment shows that the proposed approaches are effective.     

Content-based retrieval of Flash™ movies: research issues,generic framework,and future directions

As a prevailing web media format, nowadays Flash™ movies are created, delivered, and viewed by over millions of users in their daily experiences with the Internet. However, issues regarding the indexing and retrieval of Flash movies are unfortunately overlooked by the research community, which severely restrict the utilization of the extremely valuable Flash resource. A close examination reveals that the intrinsic complexity of a Flash movie, including its heterogeneous media components, its dynamic nature, and user interactivity, makes content-based Flash retrieval a host of research issues not thoroughly addressed by the existing techniques. As the first endeavor in this area, we propose a generic framework termed as FLAME (FLash Access and Management Environment) embodying a 3-layer structure that addresses the representation, indexing, and retrieval of Flash movies by mining and understanding of the movie content. An experimental prototype for Flash retrieval is implemented to verify the feasibility and effectiveness of FLAME, and future research directions on Flash management and retrieval are discussed in details.