MPEG is currently developing a standard titled Compact Descriptors for Visual Search (CDVS) for descriptor extraction and compression. In this work, we report comprehensive patch-level experiments for a direct comparison of low bitrate descriptors for visual search. For evaluating different compression schemes, we propose a dataset of matching pairs of image patches from the MPEG-CDVS image-level data sets. We propose a greedy rate allocation scheme for distributing bits across different spatialbins of the SIFT descriptor. We study a scheme based on Entropy Constrained Vector Quantization and greedy rate allocation, which performs close to the performancebound for any compression scheme. Finally, we present extensive feature-level Receiver Operating Characteristic (ROC) comparisons for different compression schemes (VectorQuantization, Transform Coding, Lattice Coding) proposed during the MPEG-CDVS standardization process.

We present the radial gradient transform (RGT) and a fast approximation, the approximate RGT (ARGT). We analyze the effects of the approximation on gradient quantization and histogramming. The ARGT is incorporated into the rotation-invariant fast feature (RIFF) algorithm. We demonstrate that, using the ARGT, RIFF extracts features 16× faster than SURF while achieving a similar performance for image matching and retrieval.

In this paper, we present a large-scale mobile augmented reality system that recognizes the buildings in the mobile device's live video and registers this live view with the 3-dimensional models of the buildings. Having the camera pose estimated and tracked, the system adds relevant information about the buildings to the video in the correct perspective. We demonstrate the system on a large database of geo-tagged panoramic images of an urban environment with associated 3-dimensional planar models. The system uses the capabilities of emerging mobile platforms such as location and orientation sensors, and computational power to detect, track, and augment buildings in urban scenes.

We survey popular data sets used in computer vision literature and point out their limitations for mobile visual search applications. To overcome many of the limitations, we propose the Stanford Mobile Visual Search data set. The data set contains camera-phone images of products, CDs, books, outdoor landmarks, business cards, text documents, museum paintings and video clips. The data set has several key characteristics lacking in existing data sets: rigid objects, widely varying lighting conditions, perspective distortion, foreground and background clutter, realistic ground-truth reference data, and query data collected from heterogeneous low and high-end camera phones. We hope that the data set will help push research forward in the field of mobile visual search.

We present a method that unifies tracking and video content recognition with applications to Mobile Augmented Reality (MAR). We introduce the Radial Gradient Transform (RGT) and an approximate RGT, yielding the Rotation-Invariant, Fast Feature (RIFF) descriptor. We demonstrate that RIFF is fast enough for real-time tracking, while robust enough for large scale retrieval tasks. At 26× the speed, our tracking-scheme obtains a more accurate global affine motion-model than the Kanade Lucas Tomasi (KLT) tracker. The same descriptors can achieve 94% retrieval accuracy from a database of 104images.

In this paper, they address a key challenge for scaling image search up to larger databases: the amount of memory consumed by the inverted index. In a VT-based image retrieval system, the most memory-intensive structure is the inverted index. For example, in a database of one million images where each image contains hundreds of features, the inverted index consumes 2.5 GB of RAM. Such large memory usage limits the ability to run other concurrent processes on the same server, such as recognition systems for other databases. A memory-congested server can exhibit swapping between main and virtual memory, which significantly slows down all processes.

We evaluate the performance of MPEG-7 image signatures, Compressed Histogram of Gradients descriptor (CHoG) and Scale Invariant Feature Transform (SIFT) descriptors for mobile visual search applications. We observe that SIFT and CHoG outperform MPEG-7 image signatures greatly in terms of feature-level Receiver Operating Characteristic (ROC) performance and image-level matching. Moreover, CHoG descriptors demonstrate such gains while being comparable with MPEG-7 image signatures in bit-rate.

In this paper, we focus on a new application of mobile visual search: snapping a photo with a mobile device of a video playing on a TV screen to automatically retrieve and stream the remainder of the video to the mobile device. When the user takes a photo of the video, the captured query frame may contain too few useful features for good retrieval performance. We design and implement a new algorithm for mobile video retrieval to accurately select a feature-rich frame from a sequence of viewfinder frames in a very short temporal window determined by the user-initiated query event. Fast and accurate selection using efficiently computed Hessian scores is developed for real-time operation on mobile devices. Viewfinder frames captured before the query starts are pre-processed, while the number of viewfinder frames captured afterwards is minimized by a probabilistic optimization process. Evaluated on a large video database of 10 million frames, dynamic query frame selection provides a substantial increase in retrieval accuracy with very low search latency.

We present a fast and efficient geometric re-ranking method that can be incorporated in a feature based image-based retrieval system that utilizes a Vocabulary Tree (VT). We form feature pairs by comparing descriptor classification paths in the VT and calculate geometric similarity score of these pairs. We propose a location geometric similarity scoring method that is invariant to rotation, scale, and translation, and can be easily incorporated in mobile visual search and augmented reality systems. We compare the performance of the location geometric scoring scheme to orientation and scale geometric scoring schemes. We show in our experiments that re-ranking schemes can substantially improve recognition accuracy. We can also reduce the worst case server latency up to 1 sec and still improve the recognition performance.

Restoring the fine detail in the slide area of a defocused lecture video is a challenging task. In this work, we propose to use clean images of slides available along with the defocused lecture video to help the restoration. Our proposed method uses local feature descriptors and multiple defocused slide decks to automatically identify the slide that is displayed in the defocused frame. We then use the matching slide as side information to estimate the parameters for deconvolution and bilateral filtering. Experimental results show that the proposed algorithm compares favorably to a computationally-intensive iterative deconvolution algorithm that does not employ any side information. In particular, it can recover small drawings and text that are severely blurred in a poorly focused lecture video

We present a mobile product recognition system for the camera-phone. By snapping a picture of a product with a camera-phone, the user can retrieve online information of the product. The product is recognized by an image-based retrieval system located on a remote server. Our database currently comprises more than one million entries, primarily products packaged in rigid boxes with printed labels, such as CDs, DVDs, and books. We extract low bit-rate descriptors from the query image and compress the location of the descriptors using location histogram coding on the camera-phone. We transmit the compressed query features, instead of a query image, to reduce the transmission delay. We use inverted index compression and fast geometric re-ranking on our database to provide a low delay image recognition response for large scale databases. Experimental timing results on different parts of the mobile product recognition system is reported in this work.

Establishing visual correspondences is an essential component of many computer vision problems, and is often done with robust, local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile distributed camera networks and large indexing problems. We propose a framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate. The framework is low complexity and has significant speed-up in the matching stage. We represent gradient histograms as tree structures which can be efficiently compressed. We show how to efficiently compute distances between descriptors in their compressed representation eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes.

For mobile image matching applications, a mobile device captures a query image, extracts descriptive features, and transmits these features wirelessly to a server. The server recognizes the query image by comparing the extracted features to its database and returns information associated with the recognition result. For slow links, query feature compression is crucial for low-latency retrieval. Previous image retrieval systems transmit compressed feature descriptors, which is well suited for pairwise image matching. For fast retrieval from large databases, however, scalable vocabulary trees are commonly employed. In this paper, we propose a rate-efficient codec designed for tree-based retrieval. By encoding a tree histogram, our codec can achieve a more than 5times rate reduction compared to sending compressed feature descriptors. By discarding the order amongst a list of features, histogram coding requires 1.5times lower rate than sending a tree node index for every feature. A statistical analysis is performed to study how the entropy of encoded symbols varies with tree depth and the number of features.

Illumination distortion due to uncontrolled lighting can severely degrade the color appearance of a photo. Frequently, the desired colors for objects in a newly taken query image are found in a previously stored database image. Then, the goal is to change the colors in the query image to match the colors in the database image. This paper presents a color restoration system that automatically retrieves a database image which matches the query image, even if the two images are taken from different viewpoints and under different illuminations. Robust features enable both accurate retrieval from the database and efficient sampling of the color differences between the query and database images. A spatially varying color mismatch model is generated, and the colors of the query image are effectively restored.

We propose a novel, low-complexity, tracking scheme that uses motion vectors directly from a video coder. We compare our tracking algorithm against ground truth data, and show that we can achieve a high level of accuracy, even though the motion vectors are rate-distortion optimized and do not represent true motion. We develop a framework for tracking in video sequences with various GOP structures. Such a scheme would find applications in the context of Mobile Augmented Reality. The proposed feature tracking algorithm can significantly reduce the required rate of feature extraction and matching.