Daniel R. Tretter

A Web-Based Secure System for the Distributed Printing of Documents and Images

We propose and consider a secure printing system for the distributed printing of documents and images over the World Wide Web. The main feature of the system is that it allows previewing and printing of selected documents and images, where only a certain number of hardcopies can be generated based on an agreed payment. The security of the system resides on an aggregate of communication protocols, smartcard technologies, and cryptographic algorithms. The system prevents eavesdropping in that people who intercept the communication cannot generate copies of the document.

Spatially Adaptive Block-Based Super-Resolution

Super-resolution technology provides an effective way to increase image resolution by incorporating additional information from successive input images or training samples. Various super-resolution algorithms have been proposed based on different assumptions, and their relative performances can differ in regions of different characteristics within a single image. Based on this observation, an adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which we incorporate reconstruction-based super-resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework. The target high-resolution image plane is divided into adaptive-sized blocks, and different suitable super-resolution algorithms are automatically selected for the blocks. Then, a deblocking process is applied to reduce block edge artifacts. A new benchmark is also utilized to measure the performance of super-resolution algorithms. Experimental results with real-life videos indicate encouraging improvements with our method.

An efficient automatic redeye detection and correction algorithm

A fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. The algorithm contains a redeye detection part and a correction part. The detection part is modeled as a feature based object detection problem. Adaboost is used to simultaneously select features and train the classifier. A new feature set is designed to address the orientation-dependency problem associated with the Haar-like features commonly used for object detection design. For each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.

Managing and searching personal photo collections

We present a prototype system for managing and searching collections of personal digital images. The system allows the collection to be stored across a mixture of local and remote computers and managed seamlessly. It provides multiple ways of organizing and viewing the same collection. It also provides a search function that uses features based on face detection and low-level color, texture and edge features combined with digital camera capture settings to provide high-quality search that is computed at the server but available from all other networked devices accessing the photo collection. Evaluations of the search facility using human relevancy experiments are provided.

Optimal transforms for multispectral and multilayer image coding

Multispectral images are composed of a series of images at differing optical wavelengths. Since these images can be quite large, they invite efficient source coding schemes for reducing storage and transmission requirements. Because multispectral images include a third (spectral) dimension with nonstationary behavior, these multilayer data sets require specialized coding techniques. The authors develop both a theory and specific methods for performing optimal transform coding of multispectral images. The theory is based on the assumption that a multispectral image may be modeled as a set of jointly stationary Gaussian random processes. Therefore, the methods may be applied to any multilayer data set which meets this assumption. Although the authors do not assume the autocorrelation has a separable form, they show that the optimal transform for coding has a partially separable structure. In particular, they prove that a coding scheme consisting of a frequency transform within each layer followed by a separate KL transform across the layers at each spatial frequency is asymptotically optimal as the block size becomes large. Two simplifications of this method are also shown to be asymptotically optimal if the data can be assumed to satisfy additional constraints. The proposed coding techniques are then implemented using subband filtering methods, and the various algorithms are tested on multispectral images to determine their relative performance characteristics.

Close & closer: social cluster and closeness from photo collections

We investigate the discovery of social clusters from consumer photo collections. Peoples participation in various social activities is the base on which social clusters are formed. The photos that record those social activities can reflect the social structure of people to a certain degree, depending on the extent of coverage of the photos on the social activities. In this paper, we propose a scheme to construct a weighted undirected graph from photo collections by examining the co-appearance of individuals in photos, wherein the weights of edges are measures of the social closeness of the involved individuals (vertices in the graph). We further apply a graph clustering algorithm that maximizes the modularity of the graph partition to detect the embedded social clusters. The experiment results demonstrate that this scheme can reveal the social cluster with high precision rate. In addition, we also introduce a few photo management capabilities enabled by the social graph and discovered social clusters.

A JPEG variable quantization method for compound documents

In this paper, we present a JPEC-compliant method for the efficient compression of compound documents using variable quantization. Based on the DCT activity of each 8 x 8 block, our scheme automatically adjusts the quantization scaling factors so that test blocks are compressed at higher quality than image blocks. Results from three different quantization mappings are also reported.

Representative Image Thumbnails for Good Browsing

Image thumbnails are commonly used for selecting images for display, sharing or printing. Standard thumbnails, generated with current techniques, do not distinguish between high and low quality originals. Both sharp and blurry originals appear sharp in the thumbnails, and both clean and noisy originals appear clean in the thumbnails. This leads to errors and inefficiencies during image selection. In this paper, thumbnails generated using image analysis better represent the local blur and the noise of the originals. The new thumbnails provide a quick, natural way for users to identify images of good quality, while allowing the viewers knowledge to select desired subject matter. Computer simulations with added blur and noise show the new thumbnails better represent images of differing qualities. Validation of these findings is found in a subjective evaluation reported elsewhere but summarized below.

Personal Clothing Retrieval on Photo Collections by Color and Attributes

Automatic personal clothing retrieval on photo collections, i.e., searching the same clothes worn by the same person, is not a trivial problem as photos are usually taken under completely uncontrolled realistic imaging conditions. Typically, the captured clothing images have large variations due to geometric deformation, occlusion, cluttered background, and photometric variability from illumination and viewpoint, which pose significant challenges to text-based or reranking-based visual search methods. In this paper, a novel framework is presented to tackle these issues by leveraging low-level features (e.g., color) and high-level features (attributes) of clothing. First, a content-based image retrieval (CBIR) approach based on the bag-of-visual-words (BOW) model is developed as our baseline system, in which a codebook is constructed from extracted dominant color patches. A reranking approach is then proposed to improve search quality by exploiting clothing attributes, including the type of clothing, sleeves, patterns, etc. Compared to low-level features, the attributes have better robustness to clothing variations, and carry semantic meanings as high-level image representations. Different visual attribute detectors are learned from large amounts of training data to extract the corresponding attributes. The construction of codebook and building of attribute classifiers are conducted offline, which leads to fast online search performance. Extensive experiments on photo collections show that the reranking algorithm based on attribute learning significantly improves retrieval performance in combination with the proposed baseline. Even our color-based baseline alone outperforms the previous CBIR-based search approaches. The experiments also demonstrate that our approach is robust to large variations of images taken in unconstrained environment.

Consumer image retrieval by estimating relation tree from family photo collections

In this paper, we propose an approach to automatically estimate relationship among people in a family image collection based on results from face analyses technologies including automated face recognition and clustering, demographic assessment, and face similarity measurement, as well as contextual information such as people co-appearance, peoples relative positions in photos and image timestamps. As the result, a relation tree can be estimated which provides important semantic information regarding people involved in a photo collection and has numerous applications in photo sharing and browsing, social networking, etc. The methods for deriving and integrating information from photos and the process for estimating a relation tree are described. Experimental results on two typical consumer photo collections and examples of using these results in consumer image retrieval are presented.