Brief Review of Recognition + ContextObject Instance RecognitionSlide 3Slide 4Category recognitionImage CategorizationObject Category DetectionSlide 8Slide 9Region-based recognitionContext in RecognitionContext provides clues for functionSlide 13Sometimes context is the major component of recognitionSlide 15More Low-ResSlide 17We will see more on context later…Brief Review of Recognition+ ContextComputer VisionCS 543 / ECE 549 University of IllinoisDerek Hoiem04/01/10Object Instance Recognition•Want to recognize the same or equivalent object instance, which may vary–Slight deformations–Change in lighting–Occlusion–Rotation, rescaling, translation, perspective===Object Instance Recognition•Template matching: faces–Recognize by directly computing pixel distance of aligned faces–Principal component analysis gives a subspace that preserves variance–Linear Discriminant Analysis (LDA) or Fisher Linear Discriminants (FLD) gives a subspace that maximizes discrimination•This could work for other kinds of aligned objectsObject Instance Recognition•If object is not aligned, we need to perform geometric matching1. Find distinctive and repeatable keypoints•E.g., Difference of Gaussian, Harris corners, or MSER regions2. Represent the appearance at these points (e.g., SIFT)3. Match pairs of keypoints4. Estimate transformation (e.g., rotation, scale, translation) from matched keypoints•Hough voting•Geometric refinement•Clustering (visual words) and inverse document frequency enable fast search in large datasetsB1B2B3A1A2A3Category recognition•Instances across categories tend to vary in more challenging ways than a single instance across imagesImage Categorization•In training, a classifier is trained for a particular feature representation using labeled examples•The features should generally capture local patterns but with loose spatial encoding•For scene categorization, a reasonable choice is often1. Compute visual words (detect interest points, represent them with SIFT, and cluster)2. Compute a spatial pyramid of these visual words, composed of histograms at different spatial resolutions3. Train a linear SVM classifier or one with a Chi-squared kernelTraining LabelsTraining ImagesClassifier TrainingImage FeaturesTrained ClassifierObject Category Detection•One difficulty of object category detection is that objects could appear at many scales or translations, and keypoint matching will be unreliable•A simple way around this is to treat category detection as a series of image categorization tasks, breaking up the image into thousands of windows and applying a binary classifier to each•Often, the object is classified using edge-based features whose positions are defined at fixed position in the sliding windowObject or Background?Object Category Detection•Sliding windows might work well for rigid objects•But some objects may be better thought of as spatial arrangements of partsObject Category Detection•Part-based models have three key components–Part definition and appearance model–Model of geometry or layout of parts–Algorithm for efficient search•ISM Model–Parts are clustered detected keypoints–Position of each part wrt object center/size is recorded–Search is done through Hough voting / Mean-shift clustering combination•Pictorial structures model–Parts are rectangles detected in silhouette–Layout is articulated model with tree-shaped graph–Search through dynamic programming or probabilistic samplingRegion-based recognition•Sometimes, we want to label image pixels or regions•Basic approach:–Segment the image into blocks, superpixels, or regions–Represent each region with histograms of keypoints, color, texture, and position–Classify each region (variety of classifiers used)Context in Recognition•Objects usually are surrounded by a scene that can provide context in the form of naerby objects, surfaces, scene category, geometry, etc.Context provides clues for function•What is this?These examples from Antonio TorralbaContext provides clues for function•What is this?•Now can you tell?Sometimes context is the major component of recognition•What is this?Sometimes context is the major component of recognition•What is this?•Now can you tell?More Low-Res•What are these blobs?More Low-Res•The same pixels! (a car)We will see more on context