2015 Seventh International Conference on Knowledge and Systems Engineering Uniform Detection in Social Image Streams Nguyen Quang Manh Nguyen Duc Tuan Dinh Viet Sang Hanoi University of Science and Technology Email: nguyenquangmanh9099@gmail.com Hanoi University of Science and Technology Email: newvalue92@gmail.com Hanoi University of Science and Technology Email: sangdv@soict.hust.edu.vn Huynh Thi Thanh Binh Nguyen Thi Thuy Hanoi University of Science Faculty of Information Technology and Technology Vietnam National University of Agriculture Email: binhht@soict.hust.edu.vn Email: ntthuy@vnua.edu.vn Abstract—Social media mining from Internet has been an emerging research topic The problem is challenging because of massive data contents from various sources, especially image data from user upload In recent years, dictionary learning based image classification has been widely studied and gained significant success In this paper, we propose a framework for automatic detection of interested uniforms in image streams from social networks The systems is composed of a powerful feature extraction module based on dense SIFT feature and a state-ofthe-art discriminative dictionary learning approach Beside that, a parallel implementation of feature extraction is deployed to make the system work real time An extensive set of experiments has been conducted on four real-life datasets The experimental results show that we can obtain the detection rate up to 100% on some datasets We also get real time performance with a speed of image stream of about 40 images per second The framework can be applied to emerging applications such as uniform detection, automated image tagging, content base image retrieval or online advertisement based on image content I Therefore, detecting interested uniforms from social images is a good way to monitor internal information related to the business of a company To the best of our knowledge, the problem of detecting uniforms (clothes) from social images for the purpose of business monitoring is an application that has not been investigated In this paper, we propose an efficient framework for detection and filtering images containing some interested uniform out of social image streams The image streams are continuously retrieved from social networks using a crawling tool An important application of social image mining is automated image tagging, which automatically assign some semantic keywords to a new image based on a pre-learned object recognition model A framework for uniform detection can be used to assign keywords to images such as company names, locations and so on Mining personal photos can also help to learn the preferences of individual users that can be used for building a recommendation system or an advertising service A framework for uniform detection can be integrated in such a system For instance, when a user viewing an image containing the uniform of some company, it means the user is likely interested in the company’s products Therefore, an advertisement for the company or a suggestion of buying some company’s products can be pushed on I NTRODUCTION In recent years, we have witnessed a rapid boom of social networking and online image sharing websites such as Facebook, Flickr, Photobucket that allow users to upload their personal photos on the web The abundant amount of information generated by social networks has stimulated people to discover useful patterns hidden in the social media data Besides text information such as statuses, comments and tags, social image data is a rich source to be exploited Mining such a huge data of social images on the web has become an emerging important research topic Many research works [5], [12], [14], [21], [24] have been proposed for mining knowledge from social images based on both visual and textual contents Up to now social images have been mined for various purposes such as mining geographic information, detecting hot events of society or finding social groups Uniform detection is a particular case of image categorization More generally, it is a two-class classification problem A general framework of image categorization usually contains two steps: feature extraction and classification After extracting features, images are represented by features which are then categorized by a pre-learned classifier Since the data may come very fast from the social image stream, a good framework for uniform detection should work fast enough to adapt to the speed of image crawling while maintaining a high detection accuracy Recently, managing the content of social images has gotten the attention from companies and organizations for ensuring the safety of their business It has been observed that social network users can unintentionally upload some images containing information that is unfavorable towards the business of a company The problem is serious when this kind of information may reveal business secrets or affect its fame In practice, such adverse images are often from company staff captured in working time when they are wearing uniform 978-1-4673-8013-3/15 $31.00 © 2015 IEEE DOI 10.1109/KSE.2015.63 Our main contributions are: (1) a combination of powerful dense SIFT with a state-of-the-art dictionary learning strategy for an efficient image feature representation; (2) an efficient system for uniform detection from social image streams as a new application of social image mining for business monitoring; and (3) four datasets of uniforms images which are made available for research community The rest of the paper is organized as follows In section 180 we briefly summarize some related work on social image mining and image classification In section we describe our proposed framework for uniform detection Experiments and evaluation on four real-life uniform datasets are shown in section The conclusion is in section with a discussion for the future work II R ELATED WORK Social image mining: With a rich source of information, social images can be mined in many ways for various applications In [6], Crandall et al proposed a method to organize 35 millions images collected from Flickr using content analysis based on textual and visual data with structural analysis based on geospatial data In [14], Luo et al combined satellite information with visual contents to recognize photo-taking environment Chen et al [5] exploited the metadata of Flickr photo including time, location and user-defined tags to analyze the distribution of photos and automatically detect hot events In [24], Yu el at proposed a method to automatically suggest photo groups based on user personal image collections Fig 1: Our framework for uniform detection (a) with the Feature Extraction module (b) constraint, have been proposed Compared with conventional DL methods, l2 -norm based DL approach not only reduces time complexity but also leads to very competitive results in image classification tasks Therefore, l2 -norm based DL approach is a good choice for our framework that meets a real time performance while maintaining a high detection rate Automated image tagging, which is an important part in many applications, is a challenging task in machine learning Conventional approach to deal with this problem is to train a classification model, e.g SVM [8], from a hand-labeled training data with a set of certain keywords Another promising approach is the search-based paradigm that is based on fast indexing and search techniques such as hashing functions [17] or scene matching [16] Recently, Pengcheng Wu et al [21] investigated a new approach to automated image tagging by learning an effective distance metric based on both visual and textual contents III O UR PROPOSED FRAMEWORK The flowchart of our framework for uniform image detection from social image streams is shown in Fig and described in details in Fig The problem of uniform detection is formulated as a classification task, wherein each image from the social data stream is classified into two classes: uniform or non-uniform For learning the system, we need to build a training dataset by crawling image data from social networks For classification, with each image, we firstly perform feature extraction using dense SIFT The feature vector is then encoded using sparse coding and spatial pooling Finally, it is categorized by a pre-learned classifier We propose to employ stateof-the-art Projective Dictionary Pair Learning (DPL) model to train our classifier Image classification with dictionary learning: In many image classification systems, usually low-level descriptors such as HOG [7] and SIFT [13] are firstly extracted at local interested points After that, these descriptors are often coded into higher dimensions by using sparse coding or vector quantization Sparse coding is an effective approach that has been successfully applied to various problems in computer vision including image classification [10], [18], [19], [22], [26] Many state-of-the-art methods in sparse coding based classification employ dictionary learning (DL) approach, where learning an overcomplete dictionary plays a critical role in the success of such methods A desired dictionary should faithfully represent the query images while supporting the discrimination of image classes Based on KSVD [1] Zhang and Li [26] proposed a joint learning algorithm named discriminative KSVD (DKSVD) incorporating classification error into the objective function of KSVD Jiang et al [10] proposed a new label consistence constraint and combined it with the reconstruction and classification errors to form a new algorithm called LCKSVD In addition to enforcing discriminative constraints on the dictionary, Yang et al [23] proposed a method called FDDL that uses Fisher criterion to make the sparse codes more discriminative A Feature extraction Feature extraction is the first step in the system and plays an important role in the success of a framework for image classification Many state-of-the-art methods for image classification firstly extract low-level image descriptors, and then transform them into mid-level features that obtain richer representation Based on this, we design a scheme for feature extraction that consists of three steps: local descriptor extraction, sparse coding and spatial pooling (Fig 1b) 1) Local descriptors: In [15], Oliva et at proposed to represent a scene image by GIST descriptors GIST presents a brief observation or a report at the first glance of a scene ignoring the object details in the scene GIST is suitable just for outdoor images In [20], Centrist descriptors that combines both local and global information in the image were proposed A drawback of Centrist descriptors is that they are not invariant to rotation In [13], Lowe proposed SIFT descriptors that are invariant to many common image deformations such as Typically, in most of the existing DL methods the standard l0 or l1 −norm sparsity constraint is imposed on the representation coefficients However, these regularizations usually lead to time-consuming training and testing phases Recently, some discriminative DL methods [9], [25], which exploit l2 -norm 181  Fig 2: The illustration architecture of our framework based on sparse coding p m Each DSIFT descriptor is then represented as a sparse linear combination of a few atoms from the dictionary D position, scale, rotation, affine transformation and illumination Hence, SIFT descriptor has been widely used in many of computer vision tasks including image classification An efficient dictionary learning method called KSVD is introduced in [1] The goal of KSVD is to minimize the reconstruction error w.r.t a given sparsity level: A development of SIFT descriptor is the dense SIFT (DSIFT) [3] Instead of computing SIFT descriptor at sparse interest points, DSIFT is computed over a dense grid in the image (Fig 3) Since more information is captured, DSIFT often leads to better results in image classification tasks In our framework we use DSIFT for the low-level image representation F X − DA D,A s.t ∀i, ≤ T, (1) where A = [a1 , a2 , , aN ] ∈ Rp×N is the matrix of sparse codes, and T is the sparsity level Theoretical results in [4] show that it would be easier to recover the underlying sparse codes of the data matrix X if we could keep large mutual incoherence between atoms during the dictionary learning phase Inspired by this idea, an improved version of KSVD called MI-KSVD was introduced in [2] The objective function of MI-KSVD is defined as follows: X − DA D,A F s.t ∀i, di +μ p i=1 p j=1 j=i = and ∀j, aj dTi dj ≤ T, (2) where μ ≥ is a hyperparameter In our framework, we use MI-KSVD to encode low-level image descriptors into higher dimensions for sparsity After learning the dictionary D, we can use it to encode the lowlevel descriptor matrix Xnew ∈ Rm×n of a new query image into a matrix Anew ∈ Rp×n of sparse vectors (Fig 2), where n is the number of DSIFT descriptors extracted from the query image Fig 3: DSIFT descriptor 2) Sparse coding: Sparse coding has been widely researched and applied to many problems of image processing It has achieved very impressive results in various image classification tasks In our framework, spare coding is used to encode DSIFT descriptors extracted from each image into an over-complete representation by learning a dictionary D on a given training set of L images 3) Spatial pooling: In order to obtain mid-level image features for a better representation, the sparse codes of lowlevel descriptors are often pooled together Spatial pooling gives us translation-invariant features by reducing the resolution of feature maps Spatial pyramid matching (SPM) [11] is a pooling scheme that has been widely used recently In SPM, the sparse codes of DSIFT descriptors are pooled in three scale levels over image space: 4×4, 2×2 and 1×1 Fig illustrates the SPM pooling scheme used in our framework Let Xl = [x1 , x2 , , xnl ] ∈ Rm×nl be the matrix of DSIFT descriptors associated with the l-th image from the training set, where xi ∈ Rm×1 is a column vector representing i-th DSIFT descriptor, m = 128 is the size of each DSIFT descriptor and nl is the number of DSIFT descriptors extracted from the l-th image Let X = [X1 , X2 , , XL ] ∈ Rm×N be the matrix of all DSIFT descriptors extracted from the training set, where N = n1 + n2 + + nL Another important role of spatial pooling is that it normalizes the dimension of the feature vectors in the dataset The diversity of social image sizes makes the number of DSIFT descriptors of an image often varied By using SPM pooling, features extracted from an image will be converted into a Suppose that we need to learn an overcomplete dictionary D = [d1 , d2 , , dp ] ∈ Rm×p that consists of p atoms, where 182 feature vector of the same size that consists of (4 × + × + × 1) × p = 21p entries the feature matrix Z = [Z1 , Z2 , , ZK ] extracted from the training set, where K is the number of classes In uniform detection problem, we have only K = classes that are uniform and non-uniform The discriminative power of DPL is given by each pair of class-specific subdictionaries {Qk ∈ R(21p)×t , Pk ∈ Rt×(21p) }, k = 1, 2, , K, where t is the number of atoms in each subdictionary The subdictionary Pk projects the data from other classes into a nearly null space while Qk tries to optimally reconstruct the feature matrix Zk from its projective matrix Pk Zk The objective function of DPL is defined as follows: In general, there are two common pooling strategies: average and max pooling Average pooling takes the average of sparse codes of DSIFT descriptors over a certain region, while max pooling computes the maximum of each entry In practice, max pooling often outperforms average pooling in various tasks Hence, in our framework we use max pooling strategy in the SPM pooling layer (Fig 4) K { Q∗ , P ∗ } = argmin +λ Pk Z¯k F F Zk − Qk Pk Zk k=1 Q,P 2 s.t qi + ≤ 1, (3) where qi is the i-th atom of Q, Zk is the training feature matrix of class k and Z¯k is the feature matrix of other classes Since the problem in (3) is non-convex, the authors in [9] relaxed it to the following problem by introducing a variable matrix U : K {P ∗ , Q∗ , U ∗ } = argmin + τ Pk Zk − P,Q Uk 2F + λ k=1 ( Zk − Qk U k Pk Z¯k F) 2 s.t qi F+ ≤ (4) The optimization problem in (4) can be easily solved by iterating the following two steps: Fig 4: Step by step of spatial pooling to obtain the feature vector from an image Step 1: Fix Q and P , update U : U ∗ = argmin 4) Parallel implementation of feature extraction: Our framework consists of two sub-systems, which are the image crawler system which crawls photos from the social networks and the system for uniform detection The first sub-system work continuously to download images from social networks, then images will be saved into a folder After a period of time (Δt) or the amount of images is large enough, the second sub-system will start working As our feature extraction involves a learning process for getting informative features, it’s a time consuming process The feature extraction process can be speed up by a parallel implementation This is done by calculating the available memory for allocating threads, which perform feature extraction for each image U K ( Zk − Qk Uk F + τ Pk Zk − Uk F ) (5) k=1 This is a quadratic optimization problem that has the following close-form solution: Uk∗ = (QTk Qk + τ I)−1 (τ Pk Zk + QTk Zk ) (6) Step 2: Fix U , update P and Q: P ∗ = argmin K Q∗ = argmin K k=1 P k=1 Q (τ Pk Zk − Uk Z k − Q k Uk F F + λ Pk Z¯k s.t qi 2 F ), ≤ (7) (8) As in [9], the closed-form of P can be obtained as follows: (9) P ∗ = τ Uk Z T (τ Zk Z T + λZ¯k Z¯T + γI)−1 B Classification k k −4 where γ = 10e In recent years, dictionary learning approaches for classification problems has received a special attention from the computer vision community Among the dictionary learning methods, the recently proposed Projective Dictionary Pair Learning (DPL) is known as one of the state-of-the-art models Unlike earlier [proposed learning methods such as DKSVD [26], LCKSVD [10], FDDL [23] that try to learn a unique dictionary for representation, DPL [9] learns jointly two separate dictionaries: a synthesis dictionary for representation power and an analysis dictionary for classification power DPL is a reasonable choice for our framework because it greatly reduces the time complexity while keeping a high accuracy k k is a small number The problem (8) can be relaxed by introducing a variable S: Q,S K k=1 Zk − Qk U k F s.t Q = S, si 2 ≤ (10) The optimal solution of (10) can be obtained by using ADMM algorithm: ⎧ K ⎪ (r+1) ⎪ ⎪ Q = argmin ( Zk −Qk Uk ⎪ ⎪ ⎪ Q ⎨ k=1 K F (r) (r) F ), + ρ Q k − S k + Tk (r+1) (r) ⎪ ρ Qk − Sk + Tk 2F s.t si 22 ≤ 1, S (r+1) = argmin ⎪ ⎪ ⎪ S ⎪ k=1 ⎪ ⎩ (r+1) (r+1) (r+1) = T ( r) + Qk − Sk , update ρ if appropriate T 1) Training DPL classifier: The essence of training DPL is to learn a synthesis dictionary Q = [Q1 , Q2 , , QK ] and an analysis dictionary P = [P1 , P2 , , PK ] to reconstruct 183 2) Classifcation scheme of DPL: Given the feature vector z of a query image, DPL will predict the class label of the image based on the class-specific reconstruction error: identif y(z) = argmin z − Qk Pk z TABLE I: The details of the collected datasets (11) k IV E XPERIMENT A Data sets To the best of our knowledge there is no public available dataset for the problem of uniform (clothes) detection Thus, we build our own datasets to evaluate the proposed framework Each dataset consists of positive and negative images A image is called positive if it contains the interested uniform, and negative otherwise These datasets will be made available for research purpose No Name of Dataset # Training images # Test images Lawson 190 245 Argentina 195 250 Brazil 195 250 Barcelona 327 394 8.0GT/s 20MB, Ubuntu Operating System 14.04 64 bit, 32GB RAM Each dataset is separated into training and testing sets This is done several times to evaluate the performance In each separation we use the training set to learn our framework and then evaluate its accuracy on the test set Table I shows the number of samples for training and testing of our datasets The overall detection accuracy is calculated by averaging the detection rates in different data separations In order to collect negative samples, we use a crawling module based on the Facebook Graph model with provided APIs to get images from public albums on many Facebook fan pages By doing that, we not violate the copyright or an individual’s privacy The collected images are very diverse since they are crawled from various categories such as landscape, street scenes, picnic or maybe selfie images Besides that, the collected images are varied in size, color, and brightness An image can contain one or more objects All the above characteristics make the datasets challenging to the classification tasks Regularization parameters of our framework such as T, τ, λ, γ are tuned based on cross-validation while others are set manually Table II shows the values of all parameters that we used in the experiment for each dataset, where dsf step is the SIFT step that was used for computing DSIFT, and other parameters are explained above TABLE II: Parameters used in our experiments In order to collect positive samples, we query some relevant keywords to the Google Search Engine and get the answers in the form of many links to the images which associated with the query From the returned links we can easily get the positive images containing the interested uniform The uniform images are in various sizes, appearing in different real-life scenes with different viewing angles Those make the uniform detection problem challenging We have downloaded four datasets of uniforms for our experiments Each dataset contains positive images that are relevant to a particular uniform Lawson dataset has the positive image set of the uniform of Lawson Company Fig illustrates the diversity of images which we got from the social network Other datasets are related to uniforms of some famous football clubs Name of Dataset dsf step p t T τ λ γ Lawson 200 30 0.01 0.001 0.001 Argentina 200 30 0.01 0.001 0.001 Brazil 200 30 0.05 0.0005 0.00001 Barcelona 200 30 0.05 0.005 0.001 C Result and Evaluation Table III shows the performance of our proposed framework in term of detection accuracy for each dataset accuracy = # true positives + # true negatives # samples (12) In average it takes about 0.025 seconds to process an image, that makes our framework suitable for real time detection of uniform from online social image stream TABLE III: The detection accuracy of our framework on four datasets Fig 5: Some samples of the Lawson dataset showing the diversity of the collected image data B Experiment setup Our experiments have been conducted using Matlab programing-language on computers with the following specifications: Intel Xeon E5-2650 v2 Eight-Core Processor 2.6GHz No Name of Dataset Accuracy(%) Lawson 100.0 Argentina 97.6 Brazil 94.0 Barcelona 97.0 Other performance measurements of our framework including precision, recall and F1 score are shown in Table IV Precision and recall are defined as follows: 184 [4] # true positives precision = # true positives + # f alse positives (13) [5] # true positives # true positives + # f alse negatives (14) [6] recall = F1 score is calculated by using precision and recall: F1 = ∗ precision ∗ recall precision + recall [7] (15) [8] TABLE IV: Evaluation of our framework in term of Recall, Precision and F-measure No Name of Dataset Precision(%) Recall(%) F1 (%) Lawson 100 100 100 Argentina 96.07 98.00 97.03 Brazil 92.93 92.00 92.46 Barcelona 96.50 95.17 95.83 [9] [10] [11] [12] As one can see, we obtained up to 100% detection rate on the Lawson dataset For other datasets, the system can detect correctly most of the images that contain at least one uniform Some missed detections are because the uniform appeared to be a small object in the image or there are objects that are similar to the interested uniform Overall, the system can give satisfaction performance for the problem of uniform detection from social images V [13] [14] [15] [16] C ONCLUSION We have proposed an efficient framework for uniform detection from images in social network The framework integrated a powerful feature descriptor DSIFT with a stateof-the-art dictionary learning for efficient image representation We have employed a max pooling layer to enhance the features for classification A parallel implementation for a fast feature extraction has been deployed Experiments have been conducted on various datasets for demonstrating the effectiveness of our framework for uniform detection The framework can be employed for recent emerging applications such as business monitoring, automated image tagging, content based image advertising [17] [18] [19] [20] [21] ACKNOWLEDGMENT [22] This research is funded 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