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Empowering visual categorization with the GPU. Present by 陳群元. outline. Introduction Overview of visual categorization Image feature extraction Category model learning Test image classification GPU accelerated categorization Experimental setup Results . introduction.
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Empowering visual categorization with the GPU Present by 陳群元 我是強壯!
outline • Introduction • Overview of visual categorization • Image feature extraction • Category model learning • Test image classification • GPU accelerated categorization • Experimental setup • Results 我是強壯!
introduction • Use GPU accelerate the quantization and classification components of a visual categorization architecture • The algorithms and their implementations should push the state-of-the-art in categorization accuracy. • Visual categorization must be decomposable into components to locate bottlenecks. • Given the same input, implementations of a component on various hardware architectures must give the same output. 我是強壯!
overview 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
Point sampling strategy • Dense sampling • Typically, around10,000 points are sampled per image • Salient point method • Harris-Laplace salient point detector [29] • Difference-of-Gaussians detector [28] 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
Descriptors • SIFT descriptor ->128 dim • 10 frames per second for 640x480 images(GPU) • SURF descriptor • 100 frames per second for 640x480 images(GPU) • ColorSIFT descriptor ->384 dim • Triple of SIFT 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
Bag-of-words • Vector quantization is computationally the most expensive part of the bag-of-words model. • Bag -> imagesset • Words->features 我是強壯!
Bag-of-words • N descriptors of length d in an image • codebook with m elements • O(ndm) per image • A tree-based codebook • O(nd log(m))->real-time on the GPU [25]. 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
Category model learning • precomputekernel function values • kernel-based SVM algorithm 我是強壯!
Support Vector Machines • Kernel Support Vector Machines 我是強壯!
Visual categorization system • Image Feature Extraction • Point Sampling Strategy • Descriptor Computation • Bag-of-Words • Category Model Learning • Test Image Classification 我是強壯!
outline • Introduction • Overview of visual categorization • Image feature extraction • Category model learning • Test image classification • GPU accelerated categorization • Parallel Programming on the GPU and CPU • GPU-Accelerated Vector Quantization • GPU-Accelerated Kernel Value Precomputation • Experimental setup • Results 我是強壯!
Parallel Programming on the GPU and CPU • SIMDinstructions perform the same operation on multiple dataelements at the same time 我是強壯!
GPU-Accelerated Vector Quantization • The most expensive computational step in vector quantizationis the calculation of the distance matrix.(n*m) • A:n*d • matrix with all image descriptors as rows • B:m*d • matrix with all codebook elements as rows 我是強壯!
GPU-Accelerated Vector Quantization(cont.) • Compute the dot products between all rows ofA and B (line 7). • matrix multiplications are the building blockfor many algorithmshighly optimized BLAS linear algebra libraries containing this operation exist for both the CPU and the GPU. 我是強壯!
GPU-Accelerated Kernel Value Precomputation • To compute kernel function values, we use the kernel function based on the distance • distance between feature vectors F and F’ • kernel function based on this distance 我是強壯!
GPU-Accelerated Kernel Value Precomputation(cont.) • multiple input features • For kernel value precomputation, memory usage is an important problem. • for a dataset with 50, 000 images, the input data is 12 GB and the output data is 19 GB • to avoid holding all data in memory simultaneously. We divide the processing into evenly sized chunks.(1024*1024) 我是強壯!
EXPERIMENTAL SETUP • Experiment 1: Vector Quantization Speed • CPU implementation is SIMD-optimized. • codebook of size m = 4, 000 • 20, 000 descriptors per image • descriptor lengths of d = 128 (SIFT) and d = 384 (ColorSIFT). • Experiment 2: Kernel Value PrecomputationSpeed • chosen the large MediamillChallenge training set of 30, 993 frames • Experiment 3: Visual Categorization Throughput • comparison is made between the quad-core Core i7 920 CPU (2.66GHz) and the GeforeGTX260 GPU (27 cores). 我是強壯!
Results • Experiment 1: Vector Quantization Speed • Experiment 2: Kernel Value PrecomputationSpeed • Experiment 3: Visual Categorization Throughput 我是強壯!
Results • Experiment 1: Vector Quantization Speed • Experiment 2: Kernel Value PrecomputationSpeed • Experiment 3: Visual Categorization Throughput 我是強壯!
Results • Experiment 1: Vector Quantization Speed • Experiment 2: Kernel Value PrecomputationSpeed • Experiment 3: Visual Categorization Throughput 我是強壯!
Results • Experiment 1: Vector Quantization Speed • Experiment 2: Kernel Value PrecomputationSpeed • Experiment 3: Visual Categorization Throughput 我是強壯!
Other applications • Application 1: k-means Clustering • Application 2: Bag-of-Words Model for Text Retrieval • Application 3: Multi-Frame Processing for Video Retrieval 我是強壯!
Conclusions • This paper provides an efficiency analysis of a state-of-the art visual categorization pipeline based on the bag-of-words model. • two large bottlenecks were identified: the vector quantization step in the image feature extraction and the kernel value computation in the category classification • Compared to a multi-threaded CPU implementation on a quad-core CPU, the GPU is 4.8 times faster. 我是強壯!
The end • Thank you! 我是強壯!
