Spectral salient object detection

© 2014 IEEE. Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection

Quantum Cuts: A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection

The increasing number of cameras, their availability to the end user and the social media platforms gave rise to the massive repositories of today’s Big Data. The largest portion of this data corresponds to unstructured image and video collections. This fact motivates the development of algorithms that would help efficient management and organization of the Big Data. This processing usually involves high level Computer Vision tasks such as object detection and recognition whose accuracy and complexity are therefore crucial. Salient object detection, which can be defined as highlighting the regions that visually stand out from the rest of the environment, can both reduce the complexity and improve the accuracy of object detection and recognition. Thus, recently there has been a growing interest in this topic. This interest is also due to many other applications of salient object detection such as media compression and summarization.This thesis focuses on this crucial problem and presents novel approaches and methods for salient object detection in digital media, using the principles of Quantum Mechanics. The contributions of this thesis can be categorized chronologically into three parts. First part is constituted of a direct application of ideas originally proposed for describing the wave nature of particles in Quantum Mechanics and expressed through Schrödinger’s Equation, to salient object detection in images. The significance of this contribution is the fact that, to the best of our knowledge, this is the first study that proposes a realizable quantum mechanical system for salient object proposals yielding an instantaneous speed in a possible physical implementation in the quantum scale.The second and main contribution of this thesis, is a spectral graph based salient object detection method, namely Quantum-Cuts. Despite the success of spectral graph based methods in many Computer Vision tasks, traditional approaches on applications of spectral graph partitioning methods offer little for the salient object detection problem which can be mapped as a foreground segmentation problem using graphs. Thus, Quantum-Cuts adopts a novel approach to spectral graph partitioning by integrating quantum mechanical concepts to Spectral Graph Theory. In particular, the probabilistic interpretation of quantum mechanical wave-functions and the unary potential fields in Quantum Mechanics when combined with the pairwise graph affinities that are widely used in Spectral Graph Theory, results into a unique optimization problem that formulates salient object detection. The optimal solution of a relaxed version of this problem is obtained via Quantum-Cuts and is proven to efficiently represent salient object regions in images.The third part of the contributions cover improvements on Quantum-Cuts by analyzing the main factors that affect its performance in salient object detection. Particularly, both unsupervised and supervised approaches are adopted in improving the exploited graph representation. The extensions on Quantum-Cuts led to computationally efficient algorithms that perform superior to the state-of-the-art in salient object detectio

Implementasi Metode Hybrid Saliency Extreme Learning Machine untuk Melakukan Saliency Detection dalam Segmentasi Citra

Saliency dari suatu objek, baik itu benda, manusia atau piksel adalah keadaan dari objek tersebut yang terlihat kontras dibandingkan dengan sekitarnya atau tetangganya. Dibutuhkan metode saliency detection yang tepat dalam segmentasi citra untuk mengidentifikasi dan memisahkan daerah yang paling menonjol atau area salient object dari suatu citra. Tugas akhir ini mengusulkan metode Saliency Extreme Learning Machine yang memadukan antara model Spectral Residual sebagai metode bottom-up dan Extreme Learning Machine (ELM) classifier sebagai metode top-down untuk melakukan segmentasi citra area salient object. Untuk menghindari pelabelan training samples secara manual, digunakan metode thresholding untuk menentukan training samples positif dan negatif dari prior saliency map yang dihasilkan oleh model Spectral Residual. Setelah training samples terbentuk dilakukan ekstraksi fitur dan saliency detection dengan ELM classifier. ELM classifier menghasilkan trained saliency map dalam empat skala superpixels yang digabungkan menjadi satu sebagai acuan dalam pembentukan object map. Uji coba yang dilakukan terhadap 50 natural images menunjukkan bahwa metode ini dapat memberikan hasil segmentasi area salient object yang akurat dengan rata-rata presisi, recall dan F1 score masing-masing sebesar 90,26%, 91,52%, dan 90,39%. ============================================================================================== Saliency of an object, be it an item, a person or a pixel is the state or quality by which it stands out relative to its neighbors. It takes the right saliency detection method in image segmentation to identify and separate the most prominent area or salient object area of an image. In this research we propose Saliency Extreme Learning Machine method which combines Spectral Residual model as bottom-up method and Extreme Learning Machine (ELM) classifier as top-down method to segment salient object area of an image. To avoid manual labeling of training samples, thresholding method is used to determine positive and negative samples from prior saliency map produced by Spectral Residual model. After training samples are formed, feature extraction and saliency detection with ELM classifier are performed. The ELM classifier generates trained saliency map in four superpixels scales that will be combined into one as a reference in forming object map. Testing conducted on 50 natural images show that this method can provide accurate segmentation of salient object area with an average of precision, recall and F1 score of 90.26%, 91.52% and 90.39% respectively

Object Discovery via Cohesion Measurement

Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure

On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection

It has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F1 score, Fb score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, as a result of debiasing Cheng et al.'s algorithm, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots)

Exploiting surroundedness for saliency detection: a boolean map approach

We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.US National Science Foundation; 1059218; 1029430http://cs-people.bu.edu/jmzhang/BMS/BMS_iccv13_preprint.pdfAccepted manuscrip