Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention

Siagian, Christian; Itti, Laurent

doi:10.1109/tpami.2007.40

Cited by 502 publications

(333 citation statements)

References 40 publications

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“…Recently, saliency detection has been widely used in many computer vision applications, and in particular, several works [39,50,51] have reported that saliency-based sampling can help to improve the performance in scene classification. Visual saliency is the perceptual ability of a vision system (e.g., human) to focus on the interesting information while ignoring irrelevant information.…”

Section: Feature Coding Based On the Saliency Mapmentioning

confidence: 99%

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

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Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

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Section: Feature Coding Based On the Saliency Mapmentioning

confidence: 99%

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

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“…Borji et al learned a Bayesian network that has some feature variables (scene gists [108], bottom-up saliency maps, game controllers, game events) connected to the corresponding eye positions and incorporated MEP (mean of the distribution of all training eye positions) as a prior distribution [109]. The approach obtained higher prediction of eye fixations than classical discriminative classifiers, including regression, support vector machine, and k nearest neighbor.…”

Section: Weighted Combination Of Bottom-up and Top-downmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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“…1) The video is partitioned into video frames, which are represented by GIST and saliency maps [11].…”

Section: A Multimedia Data Representationmentioning

confidence: 99%

Searching for Near-Duplicate Video Sequences from a Scalable Sequence Aligner

Sestrem¹,

Patrocínio²,

Guimarães

et al. 2013

2013 IEEE International Symposium on Multimedia

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Abstract-Near-duplicate video sequence identification consists in identifying real positions of a specific video clip in a video stream stored in a database. To address this problem, we propose a new approach based on a scalable sequence aligner borrowed from proteomics. Sequence alignment is performed on symbolic representations of features extracted from the input videos, based on an algorithm originally applied to bio-informatics. Experimental results demonstrate that our method performance achieved 94% recall with 100% precision, with an average searching time of about 1 second.

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Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention

Cited by 502 publications

References 40 publications

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Computational Models of Human Visual Attention and Their Implementations: A Survey

Searching for Near-Duplicate Video Sequences from a Scalable Sequence Aligner

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