We have introduced an incremental non-negative matrix factorization (INMF) scheme in order to overcome the difficulties that the conventional NMF has in online processing of large data sets. Unlike the conventional NMF, with its incremental nature and weighted cost function, the introduced INMF successfully utilizes adaptability to dynamic content changes with a low computational complexity.
The proposed scheme updates former representations by adding the effect of each new sample to the factorization incrementally, while the weighted cost function enablescontrolling the memorylessness of the factorizations. We have shown that the introduced subspace learning schemeis capable of imposing sparseness and smoothness to the factorization with the help of extra cost terms. Test results verify the INMF’s efficiency in dimension reduction and success in online factorization of large data sets.
- Proof of convergence for the update rules of INMF.
- Does NMF satisfies KKT complementarity condition at convergence? The answer is here.
Statistical Background Modeling in Video Surveillance
Background modeling in surveillance type of video is a good example to judge the dynamic content representation performance of a factorization method. This task requires representing the background scene by using a small number of background frames and then updating this representation in such a way that dynamic content changes influence the representation appropriately. These changes include entrance / leaving of an object into / from the scene and variations in object motions.
Conventionally, a quantitative measure of the dynamic content representation capability is the reconstruction error between the original frame and its reconstruction. It is expected to obtain a significant increase in the error if there is a change in the scene while the error converges to zero for the background frames. Of course this is true under the assumption that the representation of background is satisfactory.
Test results are reported to compare background modeling performances of batch-mode and incremental NMF in video surveillance. Moreover, test results obtained by the incremental PCA are also given for comparison purposes. It is shown that INMF outperforms the conventional batch-mode NMF in all aspects of dynamic background modeling. Although object tracking performance of INMF and the incremental PCA are comparable, INMF is much more robust to illumination changes.
Adaptibility To Dynamic Content ChangesIn order to justify the INMF representations’ capability of adaptation to dynamic data variations, a test video sequence which contains mobile foreground objects are used. In video surveillance applications, initially a background model is constructed and if there is a foreground object or motion in the scene, for instance if an object enters to the scene or an initially stable object in the background starts moving, the deviations from the background model are tracked. On the other hand, after a mobile object exits from the scene, or stops thus turns into a background object, a powerful online factorization scheme should be capable of adapting the representation according to these changes. These changes are referred as dynamic content changes and could be considered as local variations from the observed model. That is, the observed samples are not globally changed but only a local region of data experiences variations. So, when the samples (frames) containing foreground objects (local variations) are projected onto the background model, these deviations from the original factorization can be measured. Alternatively, after reconstructing each frame by using the factorization of the background model, the deviations from the background can be found by calculating the reconstruction error. We have constructed difference images by substituting the reconstructed image from the original frame. By this method, we aim to detect the foreground objects (if exist) in the scene.
Robustness To Illuminaton ChangesBackground modeling in outdoor surveillance video sequences may be more challenging as the illumination is very likely to change throughout the scene. The variations in the background model caused by illumination changes are global. In fact, illumination changes result in a shift at intensity values of majority of the pixels, resulting in a significant change in their mean value. Therefore, intense illumination changes are difficult to handle because of the deviations they cause on the observed data.
Inremental Learning By Incremental Non-Negative Matrix FactorizationOnce the representations (i.e. matrices W and H) are obtained for a group of observations, INMF aims to update the former representations according to the new samples. In video surveillance application, we try to model the background in the scene and update the background model as the dynamic content of the video changes. So, whenever a new farme is acquired, the mixing matrix W, which in fact reflects the characteristics of the scene, is updated by including the effect of the new frame. So for each new sample in the incremental algorithm, the mixing matrix W experiences some changes depending on the video content.
S.S. Bucak, B. Gunsel, “Online Video Scene Clustering by Competitive Incremental NMF,” Signal Image and Video Processing: 1-17 , 2011. [code]
S.S. Bucak, B. Gunsel, “Incremental Subspace Learning via Non-negative Matrix Factorization,” Pattern Recognition , vol 42(5), pp. 788-797, May 2009. [code]
S.S. Bucak, B. Gunsel, “Incremental Clustering via Nonnegative Matrix Factorization,” International Conference on Pattern Recognition (ICPR), Florida, USA, 2008. [code]
Bucak, S.S., Gunsel, B., Gursoy O., “Incremental Non-negative Matrix Factorization for Dynamic background Modelling,” ICEIS 8th International Workshop on Pattern Recognition in Information Systems (PRIS), Funchal, Portugal, 12-13 June 2007.
Bucak, S.S., Gunsel, B., Gursoy O., “Gözetleme Videolarında Artımlı Negatif Olmayan matris Ayrıştırma ile Arka Plan Modelleme (Turkish),” IEEE 15. İşaret İşleme ve İletişim Uygulamaları Kurultayı (SIU), Eskisehir, Turkey, 11-13 June 2007.
Scene Change Detection and Video Content Representation
Aim of the video scene change detection task is to be able to detect the frames where scene changes take place. These scene changes can be classified as “cuts” and “gradual changes.” Clean cuts are sudden changes between the scenes. In contrast, gradual changes, i.e., fade in/out, dissolve, wipe, etc., are generally longer and can be defined as continuous transitions between two different video scenes. Obviously, detection of the gradual changes is a more difficult task. Another difficulty in this task is avoiding false alarms which are likely to be caused by camera and object movements or lighting variations.
In the literature a number of video scene change algorithms have been reported. We have evaluated the potential use of INMF in video scene change detection. The idea behind is that if the INMF accurately represents the scene content and the dynamic changes, a significant increase in the reconstruction error should be detected at the scene change frames. Thus, determination of a video scene change is done by examining the changes on the reconstruction errors of the successive frames.
Tests are carried out on over 130000 (%24 of all the transitions were gradual) frames from the video clips recorded in TRECVID database. In order to reduce computational complexity, the INMF has been performed on the DC images of each frame. Video scene change detection performance of the INMF is evaluated in terms of two criteria: “precision” and “recall”. Precision is defined as the ratio of correct matches to the total number of transitions reported. On the other hand, recall is the number of correct matches divided by the total number of actual transitions in the video sequence. Hence, precision gives clue about the system’s false positive performance whereas recall is related to false negative ratio. As it is shown in Table 1, the number of false alarms is small, detection rates for both gradual transitions and cuts are high imposing that the INMF is a promising tool in content analysis.
It is concluded that the introduced incremental non-negative matrix factorization, with its ability to adapt the conventional NMF’s useful features to its incremental nature, is an efficient tool for modelling dynamic content in video applications. Currently we are working on derivation of sparse INMF which could be more beneficial to have more localized, parts-based representations to increase robustness to lighting variations and motion.
Table 1. Scene change detection performance of the INMF.
|True Positives #||590||174||764|
|False Negatives #||42||28||70|
|False Positives #||120|
Bucak, S.S., Gunsel, B.,” Video Content Representation by Incremental Non-negative Matrix Factorization, “ IEEE International Conference on Image Processing (ICIP), San Antonio, Texas, USA, 16-19 September 2007.
Clustering via NMF
It has been shown that (Ding & Li, 2006) non-negative matrix factorization is related to K-means clustering. After investigating this relationship and extending it to the incremental non-negative matrix factorization (INMF) scheme, we aim to implement the clustering ability of NMF on video content representation task. Formerly, we have been using reconstructing performance as a criterion in detecting scene changes for video content representation tasks. We now investigate whether the performance of the INMF in this task could be improved by adding and using its clustering feature. Our aim is to include the performance criteria used in clustering tasks into our project in order to obtain better decision criteria for scene change detection and key frame selection tasks.
Clustering approach of NMF could also be useful in understanding the nature of NMF. Our recent experiments have shown that rank selection, clustering and sparseness are related in NMF implementations. Examining clustering performance of the representations could be useful in determining the optimal rank. Hence, we are currently investigating the relationship between rank and clustering, and working on finding optimal rank selection scheme based on clustering performance criteria.