Efficient dimensionality reduction and one-class classification for content-based image retrieval

The thesis investigates various machine learning approaches to reducing data dimensionality, and studies the impact of asymmetric data on learning in image retrieval. Efficient algorithms are proposed to reduce the data dimensionality. Integration strategies for one-class classification are designed to address asymmetric data issue and improve retrieval effectiveness.

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.

An unified framework based on p-norm for feature aggregation in content- based image retrieval

Feature aggregation is a critical technique in content- based image retrieval systems that employ multiple visual features to characterize image content. In this paper, the p-norm is introduced to feature aggregation that provides a framework to unify various previous feature aggregation schemes such as linear combination, Euclidean distance, Boolean logic and decision fusion schemes in which previous schemes are instances. Some insights of the mechanism of how various aggregation schemes work are discussed through the effects of model parameters in the unified framework. Experiments show that performances vary over feature aggregation schemes that necessitates an unified framework in order to optimize the retrieval performance according to individual queries and user query concept. Revealing experimental results conducted with IAPR TC-12 ImageCLEF2006 benchmark collection that contains over 20,000 photographic images are presented and discussed.

Watermarking protocol for protecting user's right in content based image retrieval

Content based image retrieval (CBIR) is a technique to search for images relevant to the user’s query from an image collection.In last decade, most attention has been paid to improve the retrieval performance. However, there is no significant effort to investigate the security concerning in CBIR. Under the query by example (QBE) paradigm, the user supplies an image as a query and the system returns a set of retrieved results. If the query image includes user’s private information, an untrusted server provider of CBIR may distribute it illegally, which leads to the user’s right problem. In this paper, we propose an interactive watermarking protocol to address this problem. A watermark is inserted into the query image by the user in encrypted domain without knowing the exact content. The server provider of CBIR will get the watermarked query image and uses it to perform image retrieval. In case where the user finds an unauthorized copy, a watermark in the unauthorized copy will be used as evidence to prove that the user’s legal right is infringed by the server provider.

Ranking method for optimizing precision/recall of content-based image retrieval

The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to precision and recall, which are normally used to evaluate the performance of CBIR systems. In this paper, a novel ranking method based on relative density is proposed to improve the probability based approach by ranking images in the class. The proposed method can achieve optimal precision and recall. The experiments conducted on a large photographic collection show significant improvements of retrieval performance.

Content based image retrieval using unclean positive examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.

A fuzzy clustering approach to content-based image retrieval

In this study, an interactive Content-Based Image Retrieval (CBIR) system that allows searching and retrieving images from databases is designed and developed. Based on the fuzzy c-means clustering algorithm, the CBIR system fuses color and texture features in image segmentation. A technique to form compound queries based on the combined features of different images is devised. This technique allows users to have a better control on the search criteria, thus a higher retrieval performance can be achieved. A database consisting of skin cancer imagery is used to demonstrate the applicability of the CBIR system.

Secure image retrieval based on visual content and watermarking protocol

As an interesting application on cloud computing, content-based image retrieval (CBIR) has attracted a lot of attention, but the focus of previous research work was mainly on improving the retrieval performance rather than addressing security issues such as copyrights and user privacy. With an increase of security attacks in the computer networks, these security issues become critical for CBIR systems. In this paper, we propose a novel two-party watermarking protocol that can resolve the issues regarding user rights and privacy. Unlike the previously published protocols, our protocol does not require the existence of a trusted party. It exhibits three useful features: security against partial watermark removal, security in watermark verification and non-repudiation. In addition, we report an empirical research of CBIR with the security mechanism. The experimental results show that the proposed protocol is practicable and the retrieval performance will not be affected by watermarking query images.

Image retrieval based on semantics of intra-region color properties

Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. Semantics based methods have been used to describe images according to their high level features. In this paper, we performed experiments to identify the failure of existing semantics-based methods to retrieve images in a particular semantic category. We have proposed a new semantic category to describe the intra-region color feature. The proposed semantic category complements the existing high level descriptions. Experimental results confirm the effectiveness of the proposed method.

Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

Image retrieval based on bag of images

Conventional relevance feedback schemes may not be suitable to all practical applications of content-based image retrieval (CBIR), since most ordinary users would like to complete their search in a single interaction, especially on the web search. In this paper, we explore a new approach to improve the retrieval performance based on a new concept, bag of images, rather than relevance feedback. We consider that image collection comprises of image bags instead of independent individual images. Each image bag includes some relevant images with the same perceptual meaning. A theoretical case study demonstrates that image retrieval can benefit from the new concept. A number of experimental results show that the CBIR scheme based on bag of images can improve the retrieval performance dramatically.

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.

Integrating local one-class classifiers for image retrieval

In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.

Image retrieval using noisy query

In conventional content based image retrieval (CBIR) employing relevance feedback, one implicit assumption is that both pure positive and negative examples are available. However it is not always true in the practical applications of CBIR. In this paper, we address a new problem of image retrieval using several unclean positive examples, named noisy query, in which some mislabeled images or weak relevant images present. The proposed image retrieval scheme measures the image similarity by combining multiple feature distances. Incorporating data cleaning and noise tolerant classifier, a twostep strategy is proposed to handle noisy positive examples. Experiments carried out on a subset of Corel image collection show that the proposed scheme outperforms the competing image retrieval schemes.