Analysis of the essential oil of Mexican oregano (Lippia graveolens HBK)

The composition of the volatile oil of Mexican oregano (Lippia graveolens HBK) isolated by steam distillation was investigated by means of column chromatography, gas chromatography and mass spectrometry. A total of 33 components were identified including 22 hydrocarbons, 4 alcohols, 4 ethers, 2 phenols and 1 ketone. Of the 33 components observed in the present study 7 were identified in Mexican oregano for the first time.     

Deciphering Trends in Mobile Search

Understanding the needs of mobile search will help improve the user experience and increase the service's usage. An analysis of search data from a large US carrier showed that cell-phone subscribers are typing longer queries in less time and clicking on more results.     

Learning to hash: forgiving hash functions and applications

The problem of efficiently finding similar items in a large corpus of high-dimensional data points arises in many real-world tasks, such as music, image, and video retrieval. Beyond the scaling difficulties that arise with lookups in large data sets, the complexity in these domains is exacerbated by an imprecise definition of similarity. In this paper, we describe a method to learn a similarity function from only weakly labeled positive examples. Once learned, this similarity function is used as the basis of a hash function to severely constrain the number of points considered for each lookup. Tested on a large real-world audio dataset, only a tiny fraction of the points (~0.27%) are ever considered for each lookup. To increase efficiency, no comparisons in the original high-dimensional space of points are required. The performance far surpasses, in terms of both efficiency and accuracy, a state-of-the-art Locality-Sensitive-Hashing-based (LSH) technique for the same problem and data set.     

Automated image-orientation detection: a scalable boosting approach

With the proliferation of digital cameras and self-publishing of photos, automatic detection of image orientation has become an important part of photo-management systems. In this paper, we present a novel system, based on combining the outputs of hundreds of classifiers trained with AdaBoost, to determine the upright orientation of an image. We thoroughly test our system on photos gathered from professional and amateur photo collections that have been taken with a variety of cameras (digital, film, camera phones). The test images include photos that are in color and black and white, realistic and abstract, and outdoor and indoor. As this system is intended for mass consumer deployment, efficiency in use and accessibility is paramount. Results show that the presented method surpasses similar methods based on Support Vector Machines, in terms of both accuracy and feasibility of deployment.

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Mass personalization: social and interactive applications using sound-track identification

This paper describes mass personalization, a framework for combining mass media with a highly personalized Web-based experience. We introduce four applications for mass personalization: personalized content layers, ad hoc social communities, real-time popularity ratings and virtual media library services. Using the ambient audio originating from a television, the four applications are available with no more effort than simple television channel surfing. Our audio identification system does not use dedicated interactive TV hardware and does not compromise the user’s privacy. Feasibility tests of the proposed applications are provided both with controlled conversational interference and with “living-room” evaluations.     

Waveprint: Efficient wavelet-based audio fingerprinting

In this paper, we present Waveprint, a novel method for audio identification. Waveprint uses a combination of computer-vision techniques and large-scale data-stream processing algorithms to create compact fingerprints of audio data that can be efficiently matched. The resulting system has excellent identification capabilities for small snippets of audio that have been degraded in a variety of manners, including competing noise, poor recording quality and cell-phone playback. We explicitly measure the tradeoffs between performance, memory usage, and computation through extensive experimentation. The system is more efficient in terms of memory usage and computation, while being more accurate when compared with previous state of the art systems. The applications of Waveprint include song identification for end-consumer use, copyright protection for audio assets, copyright protection for television assets and synchronization of off-line audio sources, such as live television.     

Neural network-based face detection

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates