The explosive growth of multimedia data poses serious challenges to data storage, management and search. Efficient near-duplicate detection is one of the required technologies for various applications. In this paper, we introduce MyFinder, an image near-duplicate detection system for large image collections. MyFinder consists of three major components: 1) a local-feature-based image representation utilizing the proposed LDP (Local-Difference-Pattern) feature, 2) the Locality-Sensitive-Hashing (LSH) as the core indexing structure to assure the most frequent data access occurred in the main memory, and 3) multi-step verification for queries to best exclude false positives and to increase the precision.

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.

Conventional image categorization techniques primarily rely on low-level visual cues. In this paper, we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification. Specific to each image category, a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning [1]. For an image under classification which contains multiple detected text lines, we calculate a weighted Euclidian distance between each text line and the learned text concept of the target category. Subsequently, the minimum distance, along with low-level visual cues, are jointly used as the features for SVM-based classification. Experiments on a challenging image database demonstrate that the proposed fusion framework achieves a higher accuracy than the state-of-art methods for image classification.

Time-varying phenomenon, such as ripples on water, trees waving in the wind and illumination changes, produces false motions, which significantly compromises the performance of an outdoor-surveillance system. In this paper, we propose a corner-based background model to effectively detect moving-objects in challenging dynamic scenes. Specifically, the method follows a three-step process. First, we detect feature points using a Harris corner detector and represent them as SIFT-like descriptors. Second, we dynamically learn a background model and classify each extracted feature as either a background or a foreground feature. Last, a "Lucas-Kanade" feature tracker is integrated into this framework to differentiate motion-consistent foreground objects from background objects with random or repetitive motion. The key insight of our work is that a collection of SIFT-like features can effectively represent the environment and account for variations caused by natural effects with dynamic movements. Features that do not correspond to the background must therefore correspond to foreground moving objects. Our method is computational efficient and works in real-time. Experiments on challenging video clips demonstrate that the proposed method achieves a higher accuracy in detecting the foreground objects than the existing methods.

Unsolicited commercial email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. In this paper, we propose a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.

Due to variations of lighting conditions and camera hardware settings and the existence of many ethnic people with a wide range of skin colors, a generic skin model is often inadequate to accurately capture the skin distribution for individual images. In this paper, we propose an adaptive skin detection framework, which allows modeling title skin distribution with significantly higher accuracy and flexibility. First, an adaptive skin model, specific to the image under consideration and refined from the skin-similar space, is derived using a Gaussian mixture model (GMM) and standard expectation maximization (EM) algorithm. Then, we develop a support vector machine (SVM) classifier to identify the skin Gaussian from the trained GMM (with two Gaussian components) by incorporating spatial and shape information of skin pixels. Extensive experimental results performed on large image databases have demonstrated the effectiveness and benefits of the proposed approach.

With the assumption of small motion displacements in sequences, templates can be matched successfully by SSD (sum-squared-difference) optimization technique. In this paper two crucial modifications are proposed to improve the original SSD tracking. First, templates are dynamically updated for each frame. Thus, instead of designing complicated parametric models to explain various image distortions in tracking sequences, more efficient and compact parametric models, say the pure translation model, can be adopted. Second, rotation and scale can be incorporated into a translation motion model after a log-polar transformation. Extensive experimental results performed on live video sequences show the effectiveness and advantage of our approach.

We propose an adaptive skin-detection method, which allows modelling and detection of the true skin-color pixels with significantly higher accuracy and flexibility than previous methods. In principle, the proposed approach follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin-model which defines the Skin-Similar space. The Skin-Similar space often contains many non-skin pixels due to the inevitable overlap in the color space between skin pixels and some non-skin pixels under the generic skin-model. The objective of the second step is to reduce the false-positive rate by analyzing the image under consideration. Specifically, in the second step, a Gaussian Mixture Model (GMM), specific to the image under consideration and refined from its Skin-Similar space, is derived using the standard Expectation-Maximization (EM) algorithm. We then use a Support Vector Machine (SVM) classifier to identify the skin Gaussian from the trained GMM by incorporating spatial and shape information of the skin pixels. Moreover, we examine how the improvement on skin detection by this adaptive skin-model impacts the detection accuracy in the application of Objectionable Image Filtering. We further propose a two-level classification scheme based on hierarchical bagging to improve the accuracy. Results of extensive experiments on large databases demonstrate the effectiveness and benefits of our adaptive skin-model.