Vis-DSS is Licensed under the GNU GENERAL PUBLIC LICENSE. See LICENSE for more details.

1. Video Summarization

• SimpleVideoSummarizer (using Color Histogram features)
• DeepSimVideoSummarizer using Features from a Deep Model and Similarity based functions
• DeepCoverVideoSummarizer using Features from a Deep Model and Coverage Based Functions
• EntitySimVideoSummarizer using Entity Models and Features from a Deep Model and Similarity based functions
• QuerySimVideoSummarizer using Features from a Deep Model, Query Input by user and using Similarity based functions

2. Image Collection Summarization

• SimpleImageSummarizer (using Color Histogram features)
• DeepSimImageSummarizer using Features from a Deep Model and Similarity based functions
• DeepCoverImageSummarizer using Features from a Deep Model and Coverage Based Functions
• QuerySimImageSummarizer using Features from a Deep Model, Query Input by user and using Similarity based functions

3. Data Subset Selection for Image Classification

• SupervisedDSS (Supervised Data subset selection using the label information in the data subset selection)
• UnsupervisedDSS (Unsupervised Data subset selection not using the label information in data subset selection)

4. Diversified Active Learning for Image Classification

• Facility Location Functions (Representation Models)
• Disparity Min and Disparity Sum (Diversity Models)
• Set Cover and Probabilistic Set Cover (Coverage Models)
• Feature Based Functions (Coverage Models)
• Graph Cut and Saturated Coverage Functions (Representation Models)

• Budgeted Greedy Algorithm (Lazy or naive greedy algorithm under a budget, say, 60 seconds)
• Stream Greedy Algorithm (Provide a threshold for summarization, say, 0.001)
• Coverage Greedy Algorithm (Provide a coverage fraction, say, 0.9 fraction of the video)

In the case of video summarization, we support two kinds of segmentation algorithms

• Fixed Length Snippets
• Shot Detection based Snippets

• If you just want to compile and build SimpleVideoSummarizer and SimpleImageSummarizer examples, you only need OpenCV 3 (https://github.com/opencv/opencv)
• For running Deep Video Summarizer examples (with Caffe models), you will need to install Caffe (https://github.com/BVLC/caffe). You just need the CPU version of Caffe
• For running the Entity based Summarizers you might also need Dlib if you are using the feature extractor algorithms from dlib.

• Modify the CMakeLists.txt to point to yout OpenCV and Caffe locations
• mkdir build
• cd build
• cmake ..
• make

• You can download the videos from here: https://drive.google.com/drive/folders/1EjM38f8mLRfhJ3dVSMD0OlnJWO3RTvLw

1. SimpleVideoSummExample: DisparityMin with Budgeted Summarization ./SimpleVideoSummExample -videoFile <videoFileName> -videoSaveFile <videoSummaryFileName> -summaryModel 0 -segmentType 0 -summaryAlgo 0 -budget 30

2. SimpleVideoSummExample: Facility Location with Budgeted Summarization ./SimpleVideoSummExample -videoFile <videoFileName> -videoSaveFile <videoSummaryFileName> -summaryModel 2 -segmentType 0 -summaryAlgo 0 -budget 30

3. SimpleImageSummExample: DisparityMin with Budgeted Summarization ./SimpleImageSummExample -directory ~/Desktop/ivsumm/images/ -imageSaveFile ~/Desktop/ivsumm/images/summary-montage.png -summaryModel 0 -summaryAlgo 0 -budget 10 -summarygrid 100

4. DeepVideoSummExample DisparityMin with Budgeted Summarization (Using GoogleNet Scene Model) ./DeepVideoSummExample -videoFile <videoFileName> -videoSaveFile <videoSummaryFileName> -summaryModelSim 0 -simcover 0 -segmentType 0 -summaryAlgo 0 -featureLayer loss3/classifier -network_file ../../Models/googlenet_places205/deploy_places205.protxt -trained_file ../../Models/googlenet_places205/googlelet_places205_train_iter_2400000.caffemodel -mean_file ../../Models/hybridCNN/hybridCNN_mean.binaryproto -label_file ../../Models/googlenet_places205/categoryIndex_places205.csv -budget 30

5. DeepVideoSummExample: Facility Location with Budgeted Summarization (Using GoogleNet Scene Model) ./DeepVideoSummExample -videoFile <videoFileName> -videoSaveFile <videoSummaryFileName> -summaryModelSim 2 -simcover 0 -segmentType 0 -summaryAlgo 0 -featureLayer loss3/classifier -network_file ../../Models/googlenet_places205/deploy_places205.protxt -trained_file ../../Models/googlenet_places205/googlelet_places205_train_iter_2400000.caffemodel -mean_file ../../Models/hybridCNN/hybridCNN_mean.binaryproto -label_file ../../Models/googlenet_places205/categoryIndex_places205.csv -budget 30

6. DeepVideoSummExample: SetCover with Budgeted Summarization (Using GoogleNet Scene Model) ./DeepVideoSummExample -videoFile <videoFileName> -videoSaveFile <videoSummaryFileName> -summaryModelSim 0 -simcover 1 -segmentType 0 -summaryAlgo 0 -featureLayer loss3/classifier -network_file ../../Models/googlenet_places205/deploy_places205.protxt -trained_file ../../Models/googlenet_places205/googlelet_places205_train_iter_2400000.caffemodel -mean_file ../../Models/hybridCNN/hybridCNN_mean.binaryproto -label_file ../../Models/googlenet_places205/categoryIndex_places205.csv -budget 30

7. DeepImageSummExample: DisparityMin with Budgeted Summarization (Using GoogleNet Scene Model) ./DeepImageSummExample -directory ~/Desktop/ivsumm/images/ -imageSaveFile ../images/summary-montage.png -summaryModelSim 2 -simcover 0 -summaryAlgo 0 -summarygrid 100 -featureLayer loss3/classifier -network_file ../../Models/googlenet_places205/deploy_places205.protxt -trained_file ../../Models/googlenet_places205/googlelet_places205_train_iter_2400000.caffemodel -mean_file ../../Models/hybridCNN/hybridCNN_mean.binaryproto -label_file ../../Models/googlenet_places205/categoryIndex_places205.csv -budget 10

8. EntityFaceSummExample: DisparityMin with Budgeted Summarization (Using Resnet Face detection and Dlib feature extractors) ./EntityFaceSummExample -videoFile ~/Desktop/ivsumm/videos/friends.mp4 -imageSaveFile ~/Desktop/ivsumm/videos/friends-collage.png -summaryModel 0 -summaryAlgo 0 -summarygrid 60 -landmarking_model_file ~/Desktop/DNNModels/dlib/shape_predictor_5_face_landmarks.dat -pretrained_resnet_file ~/Desktop/DNNModels/dlib/dlib_face_recognition_resnet_model_v1.dat -featMode 1 -network_file_face ~/Desktop/DNNModels/ResnetFace/deploy.prototxt -trained_file_face ~/Desktop/DNNModels/ResnetFace/res10_300x300_ssd_iter_140000.caffemodel -label_file_face ~/Desktop/DNNModels/ResnetFace/labels.txt -budget 25