MULTI-LAYER AGGREGATION FOR OBJECT DETECTION
First Claim
Patent Images
1. A method for object detection, the method comprising:
- obtaining images of an object;
defining a plurality of sequential feature layers of a multi-layer feature learning network;
providing an aggregator layer receiving features from multiple layers of the multi-layer feature learning network;
optimizing, jointly and by a processor, the multi-layer feature learning network and the aggregator layer using the images of the object; and
outputting, by the processor, a set of learned features represented by the optimized multi-layer feature learning network and a detector that makes use of the generated features by the optimized aggregator layer, the set of learned features being for distinguishing the object and the detector being for classifying the object.
5 Assignments
0 Petitions
Accused Products
Abstract
Object detection uses a deep or multiple layer network to learn features for detecting the object in the image. Multiple features from different layers are aggregated to train a classifier for the object. In addition or as an alternative to feature aggregation from different layers, an initial layer may have separate learnt nodes for different regions of the image to reduce the number of free parameters. The object detection is learned or a learned object detector is applied.
49 Citations
20 Claims
-
1. A method for object detection, the method comprising:
-
obtaining images of an object; defining a plurality of sequential feature layers of a multi-layer feature learning network; providing an aggregator layer receiving features from multiple layers of the multi-layer feature learning network; optimizing, jointly and by a processor, the multi-layer feature learning network and the aggregator layer using the images of the object; and outputting, by the processor, a set of learned features represented by the optimized multi-layer feature learning network and a detector that makes use of the generated features by the optimized aggregator layer, the set of learned features being for distinguishing the object and the detector being for classifying the object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
-
-
15. In a non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for object detection, the storage medium comprising instructions for:
-
receiving an image of an object; detecting, by the processor, the object in the image with hidden features generated from hidden layers of a deep network, the hidden features learned from the hidden layers comprising different layers of abstraction, where the detecting uses an aggregation in a machine-learnt classifier, by a machine, of the hidden features from the different layers input as a feature vector; and outputting detection of the object. - View Dependent Claims (16, 17, 18, 19)
-
-
20. A method for object detection, the method comprising:
-
dividing images of an object into local parts; training, by a machine, first blocks of feature nodes to reconstruct the local parts of the images; training, by the machine, second blocks of second attribute nodes to reconstruct the feature nodes; training, by the machine, third blocks of third attribute nodes to reconstruct the second attribute nodes; and training, by the machine, a feature aggregator to classify the object, the feature aggregator trained with the second and third attribute nodes as inputs.
-
Specification