Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.Comment: ECCV 201

A New Method for the Determination of Sucrose Concentration in a Pure and Impure System: Spectrophotometric Method

Analytical chemistry is a set of procedures and techniques used to identify and quantify the composition of a sample of material. It is also focused on improvements in experimental design and the creation of new measurement tools. Analytical chemistry has broad applications to forensics, medicine, science, and engineering. The objective of this study is to develop a new method of sucrose dosage using a spectrophotometry method in a pure and impure system (presence of glucose and fructose). The work performed shows the reliability of this method. A model linking sucrose solution absorbance and mass percentage of glucose and fructose has been developed using experimental design. The results obtained show that all the investigated factors (sucrose concentration, mass percentage of glucose, and mass percentage of fructose) have a positive effect on the absorbance. The effect of the interaction between glucose and fructose on the absorbance is very significant

Efficacy of Implementing Home Care Using Eye Movement Desensitization and Reprocessing in Reducing Stress of Patients with Gastrointestinal Cancer

Background: Gastrointestinal cancer is the third most common types of cancer in the world which leads to a lot of stress among sufferers. Pharmacological and non-pharmacological approaches are used to treat stress induced by serious diseases. Eye movement desensitization and reprocessing (EMDR) technique is considered as one of non-pharmacological method for decreasing patient's stress. Objective: This study was conducted to determine the effect of home care using EMDR technique on the stress of patients with gastrointestinal cancer. Materials and Methods: The current semi-experimental study was performed on patients with gastrointestinal cancer residing in Ilam, Iran. The patients were randomly divided into two groups of intervention (n=30) and control (n=30). Home care was provided for intervention group in patients' homes which included 2 sessions (a total of 60 sessions for all patients). Each session lasted for 45 to 60 minutes according to EMDR protocol. The data were analyzed using SPSS (version 16). Results: The findings of this study showed that most of patients were male (36, 60), had diploma degrees (44, 73.3), had a monthly income less than 500 thousand (38, 63.3), were married (39, 65 ). The mean age of the patients was 69.18 +/- 11.58 years. No statistically significant difference was observed between two groups before the intervention in terms of patients' perceived stress (P>0.05). However, efficacy and perceived distress of the intervention group significantly was decreased following the intervention (P<0.05). Conclusions: According to the findings regarding the impact of home care using EMDR technique on reducing stress in patients with gastrointestinal cancer, the implementation of this intervention and provision of education for patients are recommended to expand the nursing duty to community health wards as well as to improve the health status of patients

Seroepidemiology of rubella, measles, HBV, HCV and B19 virus within women in child bearing ages (Saravan City of Sistan and Bloochastan Province)

Present survey basically focused on women between 15-45 years of age resident in a town of Sistan and Baluchistan province named as Saravan city located in border of Pakistan-Iran in order to find out the seropositivity against the viruses in child bearing ages in the above stated under study community. This descriptive cross-sectional study was carried-out from 2001 up to 2002. Saravan town was divided into 4 geographical areas and each area was further sub-divided into 10 blocks and in each block 10 families were chosen randomly. In the next step by referring to each family from the chosen married women with specified age i.e., 15-45 years, 5 mL blood was collected. Serum was then separated and stored at -20°C before the assay. ELISA kit was employed to detect anti B19, anti rubella, anti measles, anti HBV and anti HCV antibody. Furthermore during samples collection a questionnaire filled for each woman under study. This study showed that 89.6% of women understudy were seropositive against measles, rubella (96.2%), B19 (59.2%), HCV (0.8%) and HBV (19.8%), respectively. According to the results of no serious problem with rubella in this area; But, about measles, the present immunity against measles in this area is insufficient. It seems that incidence of B19 infection in this region is same as other places in Iran. The rate of seropositivity against HBV and HCV indicated of these viruses circulating in the population in this area. © 2007 Academic Journals

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

What Catches the Eye? Visualizing and Understanding Deep Saliency Models

Deep convolutional neural networks have demonstrated high performances for fixation prediction in recent years. How they achieve this, however, is less explored and they remain to be black box models. Here, we attempt to shed light on the internal structure of deep saliency models and study what features they extract for fixation prediction. Specifically, we use a simple yet powerful architecture, consisting of only one CNN and a single resolution input, combined with a new loss function for pixel-wise fixation prediction during free viewing of natural scenes. We show that our simple method is on par or better than state-of-the-art complicated saliency models. Furthermore, we propose a method, related to saliency model evaluation metrics, to visualize deep models for fixation prediction. Our method reveals the inner representations of deep models for fixation prediction and provides evidence that saliency, as experienced by humans, is likely to involve high-level semantic knowledge in addition to low-level perceptual cues. Our results can be useful to measure the gap between current saliency models and the human inter-observer model and to build new models to close this gap.Engineering and Physical Sciences Research Council (EPSRC

Human Attention in Image Captioning: Dataset and Analysis

This is the author accepted manuscript. The final version is available from IEE via the DOI in this record.Data availablility: The dataset can be found at: https://github.com/SenHe/ Human-Attention-in-Image-CaptioningIn this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences in human attention during free-viewing and image captioning tasks. We look into the relationship between human attention and language constructs during perception and sentence articulation. We also analyse attention deployment mechanisms in the top-down soft attention approach that is argued to mimic human attention in captioning tasks, and investigate whether visual saliency can help image captioning. Our study reveals that (1) human attention behaviour differs in free-viewing and image description tasks. Humans tend to fixate on a greater variety of regions under the latter task, (2) there is a strong relationship between described objects and attended objects (97% of the described objects are being attended), (3) a convolutional neural network as feature encoder accounts for human-attended regions during image captioning to a great extent (around 78%), (4) soft-attention mechanism differs from human attention, both spatially and temporally, and there is low correlation between caption scores and attention consistency scores. These indicate a large gap between humans and machines in regards to top-down attention, and (5) by integrating the soft attention model with image saliency, we can significantly improve the model's performance on Flickr30k and MSCOCO benchmarks.Engineering and Physical Sciences Research Council (EPSRC)Alan Turing Institut

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine

Machine learning methods are used today for most recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. The proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in siz