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Instructions:
Mapping New Media Objects into the IVA-Subspace
clips from each category as training data, and the rest are used as new media objects to test the performance of mapping new media objects into the IVA-Subspace.
The extracted visual features include Color Histogram (in HSV space), Edge Histogram, Texture feature based on Gray-level co-occurrence matrix, Speeded Up Robust Features (SURF) and GIST. Auditory features are made up of Centroid, Rolloff, Spectral Flux, and Root Mean Square.
We concatenate different visual features into high-dimensional vectors as input. Since audio is a kind of time series data, the dimensionalities of auditory feature vectors are inconsistent. We employ Fuzzy Clustering on auditory features in preprocessing to get isomorphic audio feature indexes [39]. As described in section 3, we use two kinds of kernels for visual-auditory correlation analysis. Specifically, we use the following radial basis function in (12), the polynomial kernel function in (13) and the sigmoid function in (14).
k x; yð Þ ¼ exp − x−yk k 2
γσ2
! ð12Þ
k x; yð Þ ¼ γ x; yh i þ cð Þn ð13Þ
k x; yð Þ ¼ tanh γ x; yh i þ cð Þ ð14Þ
where we choose empirical optimal values of γ=2, σ= 2.4 in (12), γ= 1, c= 1, n=4.2 in (13) and γ=0.6, c= 1.9 in (14), and we choose empirical optimal values of combination weights η= (0.35,0.2, 0.45) in (11).
4.2 Performance comparison results
To evaluate the efficacy of the proposed algorithm, we compare the image-audio retrieval performance of the proposed MKVARL approach with PCA [25], CCA [17] and KCCA [14] methods. When users submit an image query example which is in the training set, relevant audio clips are retrieved and returned, and vice versa. In our experiments, if a returned result and the query example are in the same semantic category, it is regarded as a correct result. And the precision performance is defined as the percentage of correctly retrieved samples in the top-k-returned results.
Figure 2 shows the Mean Average Precision (MAP) of different algorithms and Fig. 3 shows the comparison results of recall ratio. In Figs. 2 and 3, the MAP and the recall values are the average results of 10 times queries in each semantic category, including 5 times of querying image with audio examples and 5 times of querying audio with image examples. And the query examples are randomly selected. From Figs. 1 and 2 we can see that the performances of CCA, KCCA and MKVARL methods are much better than the performance of the PCA.
Meanwhile the KCCA outperforms CCA, while our proposed MKVARL algorithm gains the best performance. Above results are obtained probably because that: (1) the computing process of the projection vectors of CCA,KCCA and MKVARL is based on potential relevance between image features and audio features, it can better reflect the high-level semantics; (2) the use of kernel function in KCCA makes it more appropriate for nonlinear correlation; (3) Different kernels correspond to different notions of similarity between two data samples. In particular, in a high dimensional feature space, it is not optimal to choose one kernel for all the datasets. A single type of kernel function may fail to exploit the potential of all correlations, meanwhile multiple types kernel functions could better explore the potential of all correlations,
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which validates the importance of the proposed method. Our approach generally returns more relevant results and it verifies the effectiveness of the proposed method.
Figure 4 is a specific example of image-audio retrieval. The query example is a 5-s audio clip in the violin category. We compute the similarity score between the query audio and the images in database, and return the top-15 relevant images. The numbers below the returned images are the correlation values between the images and the audio query example. It can be seen from Fig. 4 that among the top 15 returned results there are 12 violin images.
4.3 Performance evaluation of new media objects
To test image-audio retrieval performance when query examples are out of training set, we first use the method in our previous work to estimate its coordinates in the IVA-Subspace [39], and
Fig. 3 Recall performance comparison results of image-audio retrieval
Fig. 2 MAP performance comparison results of image-audio retrieval
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then cosine distance metric to compute the cross-media correlation scores. Figures 5 and 6 are the experiment results with new query examples, including querying image by new audio and querying audio by new image. From Figs. 5 and 6 we can have the similar observation that: the overall retrieval performance with new multimedia data is good. When querying image by an
Fig. 5 Querying image by new audio
Fig. 4 An example of image-audio retrieval
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example of new audio, there are 8.58 correct results in top 20 returns on average. The performance of querying audio by new image is similar to that of querying image by new audio.
5 Conclusions
Different from most existing multimedia representation learning methods, this paper proposes multiple kernel visual-auditory representation learning framework, which learns general rep- resentation model from visual and auditory feature space by explicitly learning statistical cross- media correlations from high-dimensional kernel spaces. Besides, we design distance metric learning strategy in the mutual subspace.
The performance of our approach is tested with cross- media retrieval between image and audio data. Experiments and comparisons verify the validity, superiority and applicability of our approach from different aspects. The main limitation is that the size of image-audio database is comparatively small (lots of web image galleries are not usable because it is difficult to find suited audios). Future work includes further study on large-scale social media dataset.
Acknowledgments This research is supported by the National Natural Science Foundation of China (No.61003127, No. 61373109, No.61440016) and the China Scholarship Council (201508420248).
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Fig. 6 Querying audio by new image
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Hong Zhang corresponding author, received the BS degree from Wuhan University of Technology, China, in 2001, the MS degree from Wuhan University of Technology, China, in 2004, and PhD degree from Zhejiang University, China, in 2007. She is currently a professor in the college of computer science and technology, Wuhan University of Science and Technology, China. Her research interests include content-based multimedia analysis, machine learning and cross-media retrieval.
Wenping Zhang is currently a master student in the college of computer science and technology, Wuhan University of Science and Technology, China. He received his BS degree from Huazhong Agricultural University Chutian College, China, in 2014. His research interests include machine learning and data mining.
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Wenhe Liu received the Ms. Degree in Artificial Intelligence from The University of Edinburgh, United Kingdom, 2012. He is now a Ph.D.student with The Centre for Quantum Computation & Intelligent Systems (QCIS), the University of Technology, Sydney (UTS), Sydney, Australia. His research interests include machine learning and its applications to multimedia and computer vision.
Xin Xu received the Ph.D. degree in computer science and engineering from Shanghai Jiao Tong University, China. He is a lecturer in the School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China. His current research interests include computer vision, pattern recognition, and visual surveillance.
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Hehe Fan received the Master degree of Computer Architecture from Huazhong University of Science and Technology, China, in 2015. His research interests include distributed computing, parallel processing and machine learning. Hehe Fan is currently a Research and Development Engineer in Baidu Inc.
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Multimedia Tools & Applications is a copyright of Springer, 2016. All Rights Reserved.
Multiple kernel visual-auditory representation learning for retrieval
Abstract
Introduction
Related works
Cross-media retrieval
Multiple kernel distance metric learning
Discussion
Multiple kernel visual-auditory representation learning
Visual-auditory kernel canonical correlation analysis and mapping
Extension to multiple kernel visual-auditory analysis
Experiments
Experimental setup
Performance comparison results
Performance evaluation of new media objects
Conclusions
References
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Mapping New Media Objects into the IVA-Subspace