Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual segmentation, in a new framework called segmentation without classification. This means that segmentation of an image into regions occurs without classification of features within a region or comparison of features between regions. This segmentation framework is simpler than previous computational approaches, making it implementable by V1 mechanisms, though higher leve l visual mechanisms are needed to refine its output. However, it easily handles a class of segmentation problems that are tricky in conventional methods. The cortex computes global region boundaries by detecting the breakdown of homogeneity or translation invariance in the input, using local intra-cortical interactions mediated by the horizontal connections. The difference between contextual influences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and figure-ground segregation. The model performs segmentation in exactly the same neural circuit that solves the dual problem of the enhancement of contours, as is suggested by experimental observations. Its behavior is compared with psychophysical and physiological data on segmentation, contour enhancement, and contextual influences. We discuss the implications of segmentation without classification and the predictions of our V1 model, and relate it to other phenomena such as asymmetry in visual search.

In this study, an automatic video segmentation and classification system based on audio features has been presented. Video sequences are classified such as videos with &ldquo / speech&rdquo / , &ldquo / music&rdquo / , &ldquo / crowd&rdquo / and &ldquo / silence&rdquo / . The segments that do not belong to these regions are left as &ldquo / unclassified&rdquo / . For the silence segment detection, a simple threshold comparison method has been done on the short time energy feature of the embedded audio sequence. For the &ldquo / speech&rdquo / , &ldquo / music&rdquo / and &ldquo / crowd&rdquo / segment detection a multiclass classification scheme has been applied. For this purpose, three audio feature set have been formed, one of them is purely MPEG-7 audio features, other is the audio features that is used in [31] the last one is the combination of these two feature sets. For choosing the best feature a histogram comparison method has been used. Audio segmentation system was trained and tested with these feature sets. The evaluation results show that the Feature Set 3 that is the combination of other two feature sets gives better performance for the audio classification system. The output of the classification system is an XML file which contains MPEG-7 audio segment descriptors for the video sequence. An application scenario is given by combining the audio segmentation results with visual analysis results for getting audio-visual video segments.