In this paper we propose to effectively consider the shape and size variations for object classification. Specifically, a novel image matching method is proposed to incorporate the image segmentation with Spatial Pyramid Matching (SPM), and test our method on flower classification. A Level Influence Factor (LIF) is introduced to represent weights of different pyramid levels based on the statistical information of each segmented image. Then the images are classified based on the LIF weighted spatial pyramid bag-of-visual-words feature, and some levels with weight values zeros are not needed to be compared further. Also, in SPM matching stage, the block in one image is compared with not only its corresponding block in another image, but also the spatially neighboring blocks of the corresponding blocks to find the best match. This fuzzy matching method can incorporate some translation of objects. Experiments are performed on a flower dataset containing 1360 images from 17 different categories. And experimental results demonstrate that our proposed method has better time efficiency than traditional SPM and outperforms the state-of-art flower classification methods.

Local descriptor extraction and vector quantization are the important components of widely-used Bag-of-Features (BoF) model for visual categorization. This paper proposes a simple and efficient approach to refine the local descriptors for vector quantization by embedding semantic information. The original local descriptors are integrated by a sequence of category-independent and category-dependent basis. Particularly, the category-dependent basis is learned by minimizing the joint loss minimization over local descriptors from different categories with a shared regularization penalty, which can be formulated as a linear programming problem. The transferred descriptors are further quantized and aggregated to the visual vocabulary. Experiments are performed on PASCAL VOC 2007 benchmark and the quantitative comparisons with several state-of-the-art approaches demonstrate the effectiveness of our proposed approach.

We first propose in this paper to localize objects in images based on the models learned from the weakly labeled images. This task is termed as region of interest (ROI) detection. Local features such as SIFT or HOG are extracted and the discriminative words from clustered codewords based on SIFT and HOG are selected to model the objects. Then how to find the discriminative words to model the object is important. Existing ROI detection methods consider the information from the foreground objects by selecting the words appearing more in the images belonging to one specific image class. Considering the information from background/context is also helpful for object detection and classification, we propose to select the discriminative words which appear more in the foreground/object and less in the background/context. Second, another task is to give the class label (object in this setting) for a given image and also give the position of the object appearing in the image. This task is termed as objection detection. A normal way for this task after ROI is to extract features from the detected regions and not from the whole image. Since the discriminative words extracted during ROI detection has good discriminative ability, we propose to use these words for object detection. Experimental results on PASCAL VOC 2006 dataset and a larger dataset containing 29 classes demonstrate the effectiveness of the proposed method.

This paper addresses the problem of context-based concept fusion (CBCF) for concept detection and semantic video indexing. We introduce a novel framework based on constructing context spaces of concepts, such that the contextual correlations are used to improve the performance of concept detectors. Different from traditional CBCF approach, we present two kinds of such context spaces: explicit context space for modeling the correlation of pairwise concepts, and implicit context space for representing latent themes trained from a set of concepts. The final concept detection scores are then directly fused from explicit and implicit context spaces. Experiments are presented on TRECVid 2006 benchmark and the comparisons with several state-of-the-art approaches demonstrate the effectiveness of proposed framework.