Style-aware Mid-level Representation for Discovering Visual Connections in Space and Time
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1 Style-aware Mid-level Representation for Discovering Visual Connections in Space and Time Experiment presentation for CS3710:Visual Recognition Presenter: Zitao Liu University of Pittsburgh March 3, 2015 Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
2 Goal Goal: Discover connections. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
3 Goal Goal: Discover connections between recurring mid-level visual elements. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
4 Goal Goal: Discover connections between recurring mid-level visual elements in historic (temporal) and geographic (spatial) image collections. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
5 Goal Goal: Discover connections between recurring mid-level visual elements in historic (temporal) and geographic (spatial) image collections, and attempts to capture the underlying visual style. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
6 Visual Style Visual style: Appearance variations of the same visual element due to change in time or location. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
7 The Proposed Method Three steps: 1 Mining style-sensitive visual elements 2 Establishing correspondences 3 Training style-aware regression models Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
8 Experiment Data: 1 Three datasets are used in the paper and one dataset (CarDb) can be downloaded from the author s website. 2 CarDb: 2.96GB with 13,473 photos. (10130 for training and 3343 for testing) 3 CarDb contains cars made in crawled from Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
9 Experiment External packages: 1 VOC: extracting features. 2 LibSVM: making classification and regression. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
10 Experiment Preprocessing: CarDb decades in total. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
11 Experiment - Step1 Goal: find visual elements whose appearance correlates with the date (label). What code does: 1 Select query images from each category. 2 Random sample patches with various scales and locations from each query image. 3 Each patch is represented by a histogram of gradients (HOG). Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
12 Experiment - Step1 Goal: find visual elements whose appearance correlates with the date (label). What code does: 1 Find top N(N = 50) nearest neighbor patches in the database. foreach query decade image subsets(querydec) foreach query image in querydec foreach match decade image subsets(matchdec) foreach image in matchdec(i) foreach detector in image I More than 10K images. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
13 Experiment - Step1 Goal: find visual elements whose appearance correlates with the date (label). What code does: 1 For each cluster, compute the temporal distribution of labels. (compute entropy based on year distribution) 2 Find good clusters with peaky distributions 3 Rank good clusters and select top M (M = 80) as the discovered style-sensitive visual elements. Peaky examples: , , , , , , , , etc. Flat examples: , , , etc. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
14 Experiment - Step2 Goal: model the change in style of the same visual element over the entire label space. What code does: 1 Train a linear SVM with initial cluster images. Negative data are sampled from random Flickr images. 2 Run the learned SVM on a new subset of the data that slightly broader range in label space. 3 Keep the most confident prediction example and continue. Cross-validation is used in each step of the incremental revision. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
15 Experiment - Step2 Good positive training examples Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
16 Experiment - Step3 Goal: Prediction. What code does: 1 Train a SVM with RBF kernels on the correspondences obtained from Step 2. 2 Each training example is weighted by a transformed detection score. Keep in mind: we have 8 decades; 10 clusters for each decades; 50 neighbors in each cluster. Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
17 Thank you! Q & A Zitao Liu (University of Pittsburgh) CS3710:Visual Recognition March 3, / 17
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