Coding video sequences of visual features

Visual features provide a convenient representation of the image content, which is exploited in several applications, e.g., visual search, object tracking, etc. In several cases, visual features need to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required rate, while attaining a target efficiency for the task at hand. Although the literature has recently addressed the problem of coding local features extracted from still images, in this paper we propose, for the first time, a coding architecture designed for local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. Experimental results demonstrate that, in the case of SIFT descriptors, exploiting temporal redundancy leads to substantial gains in terms of coding efficiency

Hybrid coding of visual content and local image features

Distributed visual analysis applications, such as mobile visual search or Visual Sensor Networks (VSNs) require the transmission of visual content on a bandwidth-limited network, from a peripheral node to a processing unit. Traditionally, a Compress-Then-Analyze approach has been pursued, in which sensing nodes acquire and encode the pixel-level representation of the visual content, that is subsequently transmitted to a sink node in order to be processed. This approach might not represent the most effective solution, since several analysis applications leverage a compact representation of the content, thus resulting in an inefficient usage of network resources. Furthermore, coding artifacts might significantly impact the accuracy of the visual task at hand. To tackle such limitations, an orthogonal approach named Analyze-Then-Compress has been proposed. According to such a paradigm, sensing nodes are responsible for the extraction of visual features, that are encoded and transmitted to a sink node for further processing. In spite of improved task efficiency, such paradigm implies the central processing node not being able to reconstruct a pixel-level representation of the visual content. In this paper we propose an effective compromise between the two paradigms, namely Hybrid-Analyze-Then-Compress (HATC) that aims at jointly encoding visual content and local image features. Furthermore, we show how a target tradeoff between image quality and task accuracy might be achieved by accurately allocating the bitrate to either visual content or local features.Comment: submitted to IEEE International Conference on Image Processin

Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)

Coding local and global binary visual features extracted from video sequences

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin

Coding binary local features extracted from video sequences

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task

Coding Visual Features Extracted From Video Sequences

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