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.

Visual feedback control of a robot in an unknown environment (learning control using neural networks)

In this paper, a visual feedback control approach based on neural networks is presented for a robot with a camera installed on its end-effector to trace an object in an unknown environment. First, the one-to-one mapping relations between the image feature domain of the object to the joint angle domain of the robot are derived. Second, a method is proposed to generate a desired trajectory of the robot by measuring the image feature parameters of the object. Third, a multilayer neural network is used for off-line learning of the mapping relations so as to produce on-line the reference inputs for the robot. Fourth, a learning controller based on a multilayer neural network is designed for realizing the visual feedback control of the robot. Last, the effectiveness of the present approach is verified by tracing a curved line using a 6-degrees-of-freedom robot with a CCD camera installed on its end-effector. The present approach does not necessitate the tedious calibration of the CCD camera and the complicated coordinate transformations.

Image semantic classification by using SVM

There exists an enormous gap between low-level visual feature and high-level semantic information, and the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features. Taking this into consideration, a novel texture and edge descriptor is proposed in this paper, which can be represented with a histogram. Furthermore, with the incorporation of the color, texture and edge histograms searnlessly, the images are grouped into semantic classes using a support vector machine (SVM). Experiment results show that the combination descriptor is more discriminative than other feature descriptors such as Gabor texture.

Image featuring for retrieval of multimedia documents

The utilization of massive multimedia documents collections, such as multimedia documents in the global Internet, needs search engines which can rank using both text and image evidence. Massive size and (dynamic) nature of collection can make manual indexing prohibitively expensive in such situations. Traditional search engines utilize only text components of multimedia documents. But there are information needs, which require the utilization of image evidence. In this paper, we investigate image-feature for large and heterogeneous collections. Both the nature and complexities of information needs are key elements for an effective retrieval. Retrieval needs that depend on perceptual similarities (as found in art galleries, building architecture) require the utilization of visual cues. In such situations, the retrieval of multimedia document based on image ranking can provide higher effectiveness. Experimental results show that effectiveness of ranking based on image feature can be higher where perceptual similarities are key elements for retrieval than the retrieval effectiveness of algorithms based on text ranking algorithms

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Beyond the analogon; the "transfer of the referent" : the relationship between the external world experienced through culturally generated perceptual models constructed in the human mind, and the transfer of these images, via visual language, onto referen

Within 20th. Century art, the concept of the ‘normative’ image, as an attribute of things, has been challenged. As a consequence, paintings must now picture the ‘real’ world in other ways, incorporating knowledge and meaning beyond the analogon. Such descriptive representations were revealed as paradigmatic, rather than incontrovertible fact. Dependent on pre-conceived notions of stereotypicality, these descriptive images relied on surface illumination. My thesis explores images of things in the world as culturally inspired and information based. I examine paintings and sculptures of other cultures, such as black African, and other historical periods such as the Medieval, which reveal metonymously the basis for variations in representations of the ‘real’ world. The new enhanced representations which Modern artists created in their work were denigrated as deviant from the absolute ‘normative’ or regarded as distortions for purely mannerist and stylistic reasons. Postmodern research has reassessed them as multiple or extended imagings in whose facture new knowledge and human responses can be incorporated. These new forms of representation can be regarded as theoretical constructs rather than stylised depictions of appearance. In this way referents are transferred through the mind onto objects and vistas in the real world to align with our developed view of the physical world and better our understanding of humanity’s symbiotic relationship with nature.

The roles of object-image relative motion and static depth information in the perception of heading

Investigates visual information that enables human to effectively guide their movement through the environment. This problem is fundamental to the study of human behaviour, since survival is contingent upon the acquisition of resources that lie in different locations throughout the environment.

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.

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.

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance 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.

Classifying and detecting group behaviour from visual surveillance data

We outline an approach to classifying and detecting behaviours from surveillance data. Simple pairwise movement patterns are learned and used as building blocks to describe behaviour over a temporal sequence, or compared with other pairs to detect group behaviour. As the pair primitives are easy to redefine and learn, and complex behaviour over time is specified by the user as a sequence of pair primitives, this approach provides a flexible yet robust method of detecting complex movement in a wide variety of domains.

Monumental vision : a confrontation with the image of the Eternal Return

"Monumental Vision” is a nuanced summary of Nietzschean nihilism and the Eternal Return as rite of passage for free subjects and as condensed image of speculative intelligence proper. Utilizing Gerhard Richter’s “Sheet 692” from Atlas, a series of photographs of the mountains and lake at Sils Maria, Switzerland, as summary judgment of the limit imposed by this condition on all systems of representation, this form of vision discloses the chiasmus embedded in consciousness itself. In constantly revisiting Sils, the very location where Nietzsche “suffered” the vision of the Eternal Return, Richter has engaged repeatedly this origin for what has come into his work via Nietzsche – that is, an elective veil that refuses all compromises with transcendence until such is merged with immanence.

As situated amidst modernist “ideology as intellection”, and subsequent nascent forms of anti-modernism, the Eternal Return as image also signals the return of the Kantian “aesthetic-teleological” synthesis in non-discursive or purely visual agency. As an elective form of aesthetic vision, and as image of time insofar as it registers an overwhelming externality (Other) that nominally swallows and empowers the subject at once, this excoriating sense of universal praxis underwrites artistic and architectural production of the highest order, renegotiating concepts of the paradigmatic.

Utilizing Georg Simmel’s late work on Rembrandt (1916) and his encounter with Schopenhauer and Nietzsche (1907), the essay suggests that by the 1920s the avant-garde premises of modernism had already come under attack by an ahistorical and synoptic vision here denoted “monumental vision,” which also contains the imprint of eschatological time (invoking a schism present in rationality as such). The two readings of this image perpetrated by Karl Löwith in Nietzsche’s Philosophy of the Eternal Recurrence of the Same (Nietzsches Philosophie der ewigen Wiederkehr des Gleichen, 1935), or the cosmological and the ethical, while considered irreconcilable by Löwith, have since the 1960s been recalibrated through the figure of the event to pose possible scenarios out of the stalemate of the confrontation between Self and Other (ipseity and alterity) buried within this image as limit. In this manner, the image of the Eternal Return stands at the boundary between two forms of time (or two worlds) and signals the irreducible confrontation present in speculative thought and the necessity of closure through an aesthetic vision that produces a unitary field for all creative acts.

Notably, Nietzsche’s startling vision from Zarathustra suggests that the limit imposed by the Eternal Return is also a mask for an austere condition within subjectivity closely resembling the conundrum of Fichte’s I facing I, or thought turned toward thought itself (absolute subjectivity as cipher for Being). In Alenka Zupančič’s reading, in The Shortest Shadow: Nietzsche’s Philosophy of the Two (2003), the Eternal Return effectively contains a secret formal function that grinds all “error” to dust – a highly suggestive interpretation that also neutralizes the schism introduced by Löwith between the cosmological and the ethical.