“BING: Binarized Normed Gradients for Objectness Estimation at 300fps” is a an objectness classifier using binarized normed gradient and linear classifier, which is supported by OpenCV library.


  • Goal
    • Design a good generic objectness measure method
  • Proposed Solution
    • Simple idea: collect a few hundred object bounding boxes and a few hundred bounding boxes with random background, and train a linear SVM classifier to predict object vs no-object.
    • We observe that generic objects with well-defined closed boundaries share surprisingly strong correlation when looking at the norm of the gradient, after resizing of their corresponding image windows to small fixed size (e.g. 8x8).
    • In order to efficiently quantify the objectness of an image window, we resize it to 8x8 and use the norm of the gradients as a simple 64D feature for learning a generic objectness measure in a cascaded SVM framework.
  • Contribution
    • Propose a simple and powerful feature “BING” to help the search for objects using objectness scores.
    • Further show how the binarized version of the NG feature, namely binarized normed gradients (BING) feature, can be used for efficient objectness estimation of image windows, which reauires only a few atomic CPU operations.
    • With the PASCAL VOC2007 dataset, BING efficiently (300fps on a single laptop CPU) generates a small set of data-driven, category-independent, high quality object windows, yielding 96.2% detection rate (DR) with 1,000 windows.
    • For the generalization ability, when training BING on 6 object categories and testing on other 14 unseen categories, we observed similar high performance as in standard settings.


  • Objectness: a vlaue which reflects how likely an image window covers an object of any category.
  • Designing a good generic objectness measure method should:
    • achieve high object detection rate (DR), as any undetected objects at this stage cannot be recovered later
    • produce a small number of proposals for reducing computational time of subsequent detectors
    • obtain high computational efficiency so that the method can be easily involved in various applications, especially for realtime and large-scale applications
    • have good generalization ability to unseen object categories, so that the proposals can be reused by many category specific detectors to greatly reduct the computation for each of them



  • When resizing windows corresponding to real world objects to a small fixed size, the norm (i.e. magnitude) of the corresponding image gradients gradients becomes a good discriminative feature, because of the little variation that closed boundaries could present in such abstracted view.
  • Image gradient is a directional change in the intensity or color in an image.

Learning objectness measurement

  1. Firstly, resize the input image to different quantized sizes and calculate the normed gradients of each resized image.
    • The values in an 8x8 region of these resized normed gradients maps are defined as a 64D normed gradients (NG) feature of its corresponding window.
  2. Learn a single model using linear SVM.
    • NG features of the ground truth object windows and random sampled background windows are used as positive and negative training samples respectively.
  3. Learn coefficient and bias terms for each quantised size for objectness score



  • PASCAL VOC2007 test set
    • 4,952 images with bounding box annotation for the object instances from 20 categories



DR-#WIN curves

  • Performance is evaluated with curves measuring the detection-rate vs number of windows (DR-#WIN).
  • #WIN is the number of windows output by the algorithm being evaluated.
  • DR is the percentage of ground-truth objects covered by those windows.
  • An object is considered covered by a window if the strict PASCAL-overlap criterion is satisfied (intersection-over-union(INT-UION) > 0.5).
  • For comparing methods, we summarize a DR-#WIN curve with a single value: the area under the curve (AUC), after renormalizing the horizontal axis from [0, 1000] to [0,1], so the AUC ranges in [0,1].

  • Refer: Measuring the objectness of image windows

Experimental Evaluation


Proposal quality comparisons

  • BING achieves 99.5% DR using only 5,000 proposals by simply collecting the results from 3 color spaces (BING-diversified): RGB, HSV, and GRAY.

Generalize ability test

  • BING are generic over categories by testing BING on images containing objects whose categories are not used for training.
  • Specifically, we train BING using 6 object categories and test it using the rest 14 categories.
  • The statistics for training and testing on same or different object categories are represented by BING and BING-generic, respectively.
  • Both are almost identical, which demonstrates the generalize ability of our proposals.

Computational Time


Qualitative Analysis


Quantitative Analysis