Guoping Qiu

Robust Detection of Region-Duplication Forgery in Digital Image

Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image in order to conceal an important object in the scene, is one of the common image forgery techniques. In this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. We present experimental results which show that our method is robust and can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations

Color image indexing using BTC

This paper presents a new application of a well-studied image coding technique, namely block truncation coding (BTC). It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases. From the BTC compressed stream (without performing decoding), we derive two image content description features, one termed the block color co-occurrence matrix (BCCM) and the other block pattern histogram (BPH). We use BCCM and BPH to compute the similarity measures of images for content-based image retrieval applications. Experimental results are presented which demonstrate that BCCM and BPH are comparable to similar state of the art techniques.

JPEG Error Analysis and Its Applications to Digital Image Forensics

JPEG is one of the most extensively used image formats. Understanding the inherent characteristics of JPEG may play a useful role in digital image forensics. In this paper, we introduce JPEG error analysis to the study of image forensics. The main errors of JPEG include quantization, rounding, and truncation errors. Through theoretically analyzing the effects of these errors on single and double JPEG compression, we have developed three novel schemes for image forensics including identifying whether a bitmap image has previously been JPEG compressed, estimating the quantization steps of a JPEG image, and detecting the quantization table of a JPEG image. Extensive experimental results show that our new methods significantly outperform existing techniques especially for the images of small sizes. We also show that the new method can reliably detect JPEG image blocks which are as small as 8 × 8 pixels and compressed with quality factors as high as 98. This performance is important for analyzing and locating small tampered regions within a composite image.

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.

A new key frame representation for video segment retrieval

In this paper, we propose an optimal key frame representation scheme based on global statistics for video shot retrieval. Each pixel in this optimal key frame is constructed by considering the probability of occurrence of those pixels at the corresponding pixel position among the frames in a video shot. Therefore, this constructed key frame is called temporally maximum occurrence frame (TMOF), which is an optimal representation of all the frames in a video shot. The retrieval performance of this representation scheme is further improved by considering the k pixel values with the largest probabilities of occurrence and the highest peaks of the probability distribution of occurrence at each pixel position for a video shot. The corresponding schemes are called k-TMOF and k-pTMOF, respectively. These key frame representation schemes are compared to other histogram-based techniques for video shot representation and retrieval. In the experiments, three video sequences in the MPEG-7 content set were used to evaluate the performances of the different key frame representation schemes. Experimental results show that our proposed representations outperform the alpha-trimmed average histogram for video retrieval.

Object motion detection using information theoretic spatio-temporal saliency

This paper proposes to employ the visual saliency for moving object detection via direct analysis from videos. Object saliency is represented by an information saliency map (ISM), which is calculated from spatio-temporal volumes. Both spatial and temporal saliencies are calculated and a dynamic fusion method developed for combination. We use dimensionality reduction and kernel density estimation to develop an efficient information theoretic based procedure for constructing the ISM. The ISM is then used for detecting foreground objects. Three publicly available visual surveillance databases, namely CAVIAR, PETS and OTCBVS-BENCH are selected for evaluation. Experimental results show that the proposed method is robust for both fast and slow moving object detection under illumination changes. The average detection rates are 95.42% and 95.81% while the false detection rates are 2.06% and 2.40% in CAVIAR (INRIA entrance hall and shopping center) dataset and OTCBVS-BENCH database, respectively. The average processing speed is 6.6fps with frame resolution 320x240 in a typical Pentium IV computer.

Tone-mapping high dynamic range images by novel histogram adjustment

In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

Advances in Visual Information Systems

Keynote Paper.- Visual Information Retrieval - Future Directions and Grand Challenges.- Image and Video Retrieval.- Approximation-Based Keypoints in Colour Images - A Tool for Building and Searching Visual Databases.- A Knowledge Synthesizing Approach for Classification of Visual Information.- Image Similarity - From Fuzzy Sets to Color Image Applications.- A Semi-automatic Feature Selecting Method for Sports Video Highlight Annotation.- Face Image Retrieval System Using TFV and Combination of Subimages.- Near-Duplicate Detection Using a New Framework of Constructing Accurate Affine Invariant Regions.- Where Are Focused Places of a Photo?.- Region Based Image Retrieval Incorporated with Camera Metadata.- Empirical Investigations on Benchmark Tasks for Automatic Image Annotation.- Automatic Detection and Recognition of Players in Soccer Videos.- A Temporal and Visual Analysis-Based Approach to Commercial Detection in News Video.- Salient Region Filtering for Background Subtraction.- A Novel SVM-Based Method for Moving Video Objects Recognition.- Image Classification and Indexing by EM Based Multiple-Instance Learning.- Visual Biometrics.- Palm Vein Extraction and Matching for Personal Authentication.- A SVM Face Recognition Method Based on Optimized Gabor Features.- Palmprint Identification Using Pairwise Relative Angle and EMD.- Finding Lips in Unconstrained Imagery for Improved Automatic Speech Recognition.- Intelligent Visual Information Processing.- Feature Selection for Identifying Critical Variables of Principal Components Based on K-Nearest Neighbor Rule.- Denoising Saliency Map for Region of Interest Extraction.- Cumulative Global Distance for Dimension Reduction in Handwritten Digits Database.- A New Video Compression Algorithm for Very Low Bandwidth Using Curve Fitting Method.- The Influence of Perceived Quality by Adjusting Frames Per Second and Bits Per Frame Under the Limited Bandwidth.- An Evolutionary Approach to Inverse Gray Level Quantization.- Visual Data Mining.- Mining Large-Scale News Video Database Via Knowledge Visualization.- Visualization of the Critical Patterns of Missing Values in Classification Data.- Visualizing Unstructured Text Sequences Using Iterative Visual Clustering.- Enhanced Visual Separation of Clusters by M-Mapping to Facilitate Cluster Analysis.- Multimedia Data Mining and Searching Through Dynamic Index Evolution.- Ubiquitous and Mobile Visual Information Systems.- Clustering and Visualizing Audiovisual Dataset on Mobile Devices in a Topic-Oriented Manner.- Adaptive Video Presentation for Small Display While Maximize Visual Information.- An Efficient Compression Technique for a Multi-dimensional Index in Main Memory.- RELT - Visualizing Trees on Mobile Devices.- Auto-generation of Geographic Cognitive Maps for Browsing Personal Multimedia.- Semantics.- Automatic Image Annotation for Semantic Image Retrieval.- Collaterally Cued Labelling Framework Underpinning Semantic-Level Visual Content Descriptor.- Investigating Automatic Semantic Processing Effects in Selective Attention for Just-in-Time Information Retrieval Systems.- News Video Retrieval by Learning Multimodal Semantic Information.- 2D/3D Graphical Visual Data Retrieval.- Visualization of Relational Structure Among Scientific Articles.- 3D Model Retrieval Based on Multi-Shell Extended Gaussian Image.- Neurovision with Resilient Neural Networks.- Applications of Visual Information Systems.- Visual Information for Firearm Identification by Digital Holography.- GIS-Based Lunar Exploration Information System in China.- Semantic 3D CAD and Its Applications in Construction Industry - An Outlook of Construction Data Visualization.- A Fast Algorithm for License Plate Detection.- Applying Local Cooccurring Patterns for Object Detection from Aerial Images.- Enticing Sociability in an Intelligent Coffee Corner.- Geometric and Haptic Modelling of Textile Artefacts.- A Toolkit to Support Dynamic Social Network Visualization.- The Predicate Tree - A Metaphor for Visually Describing Complex Boolean Queries.- Potentialities of Chorems as Visual Summaries of Geographic Databases Contents.- Compound Geospatial Object Detection in an Aerial Image.- Texture Representation and Retrieval Using the Causal Autoregressive Model.- An Approach Based on Multiple Representations and Multiple Queries for Invariant Image Retrieval.

From Local Pixel Structure to Global Image Super-Resolution: A New Face Hallucination Framework

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution (LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution (HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the framework. Step 1 searches the database for example faces that are the most similar to the input, and then warps the example images to the input using optical flow. Step 2 uses the warped HR version of the example faces to learn the local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face, and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping errors, have been developed. Step 3 estimates the target HR face by solving a constrained optimization problem by means of an iterative procedure. Experimental results show that our new method can provide good performances for face hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art methods.